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Class-probability plots reconstruct uncertainty on complete training and evaluation rows, then restore omitted display positions. Bootstrap, simultaneous and derivative intervals now support fitted objects with missing rows without changing complete-input results or re-evaluating data and NA callbacks.
Conditional density and distribution perspective plots retain undefined targets and range automatic display limits over finite estimates and interval endpoints. Explicit limits and returned numerical results are unchanged.
Stored conditional bandwidth constructors and native consistent-model tests reject unused formula-only frame controls without evaluating them. Formula subset and NA handling retain their existing owner.
Restoring user options no longer rolls back the worker acceleration agreement certificate; failed delivery leaves that certificate invalid.
Package-generated bandwidth-selector calls retain their package owner under caller masking and either np/npRmpi attachment order. Argument and formula environments remain with their original callers.
Density plot empty-window admission respects the effective uniform kernel when a supplied kernel order is ignored.
Density surface ranges include the selected bias-corrected center and its intervals; explicit ranges and returned plot data are unchanged.
Clarify the experimental pilot-scale limitations of compact density normal intervals and whole-panel unavailability of simultaneous variability bands.
Single-index formula fits retain their training and evaluation NA policies without forwarding already-consumed formula controls to native constructors.
Prepared bandwidth searches and automatic calls now publish worker-option agreement only after successful delivery. Restoring earlier options cannot reuse a stale agreement record; synchronization policies and RNG guards are unchanged.
Document known experimental coverage limitations of pilot-based normal-scale bias-corrected density intervals, including potentially substantial uncertainty understatement. All bias-corrected bounds remain pre-beta; calculations, returned objects and defaults are unchanged.
Isolated plot samplers now preserve and validate R-devel’s fourth RNG kind (binomial selection). Existing generators, stream receipts and RNG safety guards are unchanged.
Temporary RNG scopes and regression percentile task validation accept the documented binomial RNG kind on newer R runtimes. Existing hidden-state refusals, selected generators and caller-state restoration are preserved.
MPI plot-data and density-bias examples follow the existing non-running ordinary-check policy and remain in the executable documentation gate.
Fixed-bound two-dimensional uniform and Epanechnikov density plots add asymptotic calibrated pointwise/Bonferroni bands and independent symmetric-sd and asymmetric-sd bands across all four explicit band types. Joint guide dependence, literal bootstrap refits and a complete common quadrature are retained within bounded plot workspaces. Existing normal and univariate methods, core estimators and se=FALSE are unchanged. Asymptotic q2 simultaneous/all calibration remains unavailable in this addition.
Fixed-bound q2 uniform and Epanechnikov density plots now support asymptotic normal (pmzsd or omitted-band) intervals using the unchanged corrected center and complete joint ratio-pilot variance. Target-specific blocked quadrature supports full default grids within a bounded plot workspace. Existing calibrated/SD methods, core estimators and se=FALSE remain unchanged.
Automatic plot task and regression-hat dispatch now apply the existing option-agreement policy even after temporary plot localization. Manual MPI and genuinely local calculations retain their existing ownership.
Fixed-bound q1 uniform and Epanechnikov density plots support asymptotic pmzsd intervals, including an omitted band, with the existing corrected center and complete ratio-pilot normal variance. Cross-guide weights retain their pre-underflow log probabilities. Calibrated bands, core estimators and se=FALSE retain their existing behavior.
Two-dimensional fixed-bound beta normal plot intervals now admit larger target grids using the existing blocked calculation and a complete workspace bound. The 192 MiB limit, statistical method, tolerances and existing successful paths are unchanged.
Fixed-bound beta density plots now support two-dimensional asymptotic pmzsd intervals with complete joint ratio-pilot variance and the existing corrected center. Calibrated bands, core estimators and se=FALSE are unchanged.
Estimate-centered uniform density joint plot bands now admit known one-sided and partially bounded supports in one or two continuous dimensions, preserving the fitted curve and marginal standard errors.
Plot grid controls now use both requested quantile endpoints on each numeric X margin, rather than recycling them as symmetric trim amounts. Default grids, categorical levels and legacy internal trim inputs are unchanged.
Single-index plot grids now honor the requested quantile range of
the fitted index. Slice-holding xq controls are rejected
for this single-axis plot.
Formula estimator calls now reuse a supplied numeric bandwidth expression once, including side effects and random expressions.
The plot-only normal reference guide now satisfies both existing score certificates before stopping. A rare premature numerical stop is corrected without changing the method, tolerances, fitted center or se=FALSE.
Positive-Gram covariances in corrected bounded-density simultaneous plots that fail the raw absolute eigenvalue check are now tested by the existing normalized-correlation factor and reconstruction checks. Successful paths, covariance values, random streams and calibration are unchanged.
Align generalized nearest-neighbor regression all-large CV shortcuts with serial np, including local polynomial fits. Existing literal-kernel paths, conditional bandwidth policies and RNG safeguards are unchanged.
Formula selectors and conditional/grid model frames consume original data and NA-policy promises once, preserving saved calls, subset evaluation, RNG state ownership and MPI behavior.
Known-fixed-bound univariate uniform and Epanechnikov density plots now support explicit corrected asymptotic pointwise, Bonferroni, simultaneous and all bands through a plot-only local-cubic guide/residual construction. Existing methods, core estimates and se=FALSE are unchanged. The same explicit band choices also support bootstrap=“inid” with symmetric-sd or asymmetric-sd calibration. This plot-only addition refits the guide in each literal resample and bootstraps the residual functional at the original empirical scale, enveloping complete pilot/empirical intervals. Saved-sample screening is substantially conservative; singular-edge intervals can be wide and finite-draw tail-resolution warnings remain. Defaults, range bounds, literal endpoints, pmzsd and multivariate compact correction are unchanged.
Smooth-coefficient automatic degree searches admit temporary RNG state on every participating rank before entering local work. A worker-only unsafe state now produces the existing coordinated refusal instead of stranding peers; supported streams and caller-state restoration are unchanged.
MPI session reinitialization preserves open attach-pool ownership and reuses its workers without repeating startup collectives. Invalid profile-to-attach conversion fails before altering the pool.
Automatic-degree regression and conditional density/distribution searches use the existing all-rank command context to select one collective search owner. Explicit manual broadcasts remain coherent after pool adoption; rank-local services, ordinary automatic dispatch and both RNG safety guards are preserved.
Fixed-bound univariate density plot bands now resolve narrow corrected-score means and guide projections on the same positive quadrature measure as their covariance. Statistical formulas, tolerances and core estimates are unchanged.
Formula/IV argument forwarding reads bandwidth fields by exact
name, so bwsolver cannot be mistaken for bws
or suppress the requested IV seed. Existing bandwidth objects, solver
choices and numerical algorithms are unchanged.
Single-index NOMAD searches agree on temporary RNG admission and activation before workers enter the objective service. Coordinator-owned starts, payload assembly, Powell refinement and verification retain local RNG restoration without unmatched collectives; existing guards and worker computation remain.
Package-owned MPI startup and reuse check loaded R/native source identities before accepting a worker pool. Missing or mismatched identities give a restart/reinstall error; same-source builds at different paths/platforms are allowed. This startup check does not change RNG guards or numerical methods.
Temporary RNG scopes validate caller state before resetting or replacing it, register cleanup before activation, and restore supported built-in state without resetting the generator kind. Fresh sessions and workers without a seed binding are supported for certifiable built-in generators, with original absence and kind restored. Active or locked bindings and uncertifiable Box-Muller/custom state remain refused. Deterministic uniform-only transport retains its qualified normal-state paths; unaffected direct draws and existing statistical calculations are unchanged. Enumerated collective numerical entries agree on refusal before computation; the manual receiver handles this specific completed refusal and remains reusable.
Regression data plots inside manual MPI broadcasts preserve the existing collective owner on every rank; plotting after closing a pool retains its local fallback.
Copula plots now retain matching missing interval endpoints on repeated coordinates. Finite endpoint rounding tolerances and returned data are unchanged.
RNG replay: resolved regression, density and CDF producers now refuse repeated draws when custom uniform state is provably absent from the saved seed. Retained innovation caches and explicit replayable streams are preserved.
Treat an absent visible seed as missing custom-uniform replay state, consistently with a header-only seed. Existing safe paths and active-binding non-evaluation are preserved.
Refuse repeated uncached Box-Muller normal replay in corrected regression, density and distribution calibration, including geometric resampling. Actual-value caches, normal-free laws and one-pass calculations are retained; saved Inversion streams remain usable regardless of the caller’s normal kind.
Improve plot-only univariate fixed-bound beta/Gaussian corrected-band second-moment quadrature, including asymptotic normal pmzsd intervals. Resolve narrow variance features using a common positive quadrature rule, retaining the original independent refinement check, corrected centers, guide fits and numerical tolerances. Estimators and skip-SE paths are unchanged.
Extend estimate-centered asymptotic joint variability bands to fixed higher-order beta kernels (orders 4, 6 and 8), with finite known bounds and one/two continuous dimensions. Preserve signed contributions, native marginal scales and all existing corrected beta methods.
Estimate-centered density plots now provide finite-grid asymptotic joint variability bands for fixed uniform kernels in one/two continuous dimensions, with unbounded or finite known support. Existing marginal bands and estimators are unchanged; unidentified correlation remains explicitly unavailable.
Clarify explicit adoption of an existing profile-started MPI pool before master plotting, and add an opt-in lifecycle/plot sentinel. Implementation, estimator and uncertainty calculations are unchanged.
Add plot-only asymptotic normal pmzsd intervals for univariate fixed-bound order-2 beta and Gaussian densities, using complete ratio-pilot variance around the unchanged corrected center. Existing calibrated bands, core estimators and skip-SE paths are preserved; q2/bootstrap pmzsd remains outside this addition.
Refuse resolved unsigned fixed-block CDF replay across target tiles when a custom uniform generator supplies no replayable seed state.
MPI bootstrap tasks that require normal draws now refuse seed-only Box-Muller replay explicitly; precomputed innovations and normal-free tasks are unchanged.
Refuse uncached multi-tile signed fixed/geometric CDF bootstrap replay when a custom uniform generator exposes no replay state, instead of combining different observation-count draws across targets. Existing cached, single-tile and complete-state paths are unchanged.
Plot calibration help now distinguishes visible seed restoration from hidden generator state and subsequent caller draws. No RNG behavior is changed.
Multi-tile numeric joint-CDF geometric bootstrap plots with unsigned calibration now refuse custom uniform state absent from the visible seed; signed cached paths and full-state generators retain their existing behavior.
Explicit tiled density/CDF factor plots now refuse a custom uniform generator whose visible seed omits its replay state, before entering the plot seed scope.
Bias-corrected regression plots now detect unsafe uncached Box-Muller replay for retained formulas with quoted predictor names, before changing RNG state.
Add experimental plot-only FG-D corrected intervals for fixed, known-bound order-2 Gaussian density kernels in one or two continuous dimensions: asymptotic, symmetric-sd and asymmetric-sd, with explicit pointwise or simultaneous bands. Combine complete pilot and empirical residual intervals, preserving signed inversion, guide uncertainty and full-grid calibration. Range-derived bounds and literal endpoints are rejected. Core estimators, se=FALSE, explicit saved controls, unbounded D8 and beta inference are unchanged.
Refuse unsafe Box-Muller replay for explicit joint-scale and retained version-2 density factor bands above 512 targets. Default D8, covariance and bootstrap providers retain their existing computation and RNG behavior.
Refuse unsafe Box-Muller replay before seeding for explicit CDF simultaneous factor bands above 32 targets; covariance and deterministic bands are unchanged.
Recognize ordinary retained native and formula training values when checking uncached corrected-regression Box-Muller replay. Explicit data overrides keep precedence; no stored call, model frame or user NA policy is reevaluated.
Refuse selected corrected regression and multi-tile fixed numeric joint CDF plots when a custom uniform generator exposes only a seed header and no replay state. This pre-seed check preserves the selected RNG; a nonempty payload is not a general certificate for arbitrary custom generators.
Refuse known uncached Box-Muller replay before seeding corrected plot inference: Gaussian regression above the 16 MiB innovation limit when plain training data are available, and multi-tile fixed numeric joint CDF inference. Qualified cached and deterministic paths are preserved; other replay limitations remain.
Bounded Gaussian bias-corrected regression calibration now reuses actual Box-Muller innovations across target tiles and worker tasks. This preserves joint studentization for built-in uniform generators when the innovation matrix is at most 16 MiB. Larger replay jobs, opaque custom-generator state and caller hidden-state restoration remain unresolved separate limitations.
Small-grid Gaussian density scale envelopes now preserve a single normal sample, avoiding Box-Muller replay errors and incorrect signed maxima. Broader tiled replay and caller-RNG restoration remain separate limitations.
MPI autodispatch now delivers named normal-reference,
uniform-kernel-order, and unavailable-variance warnings after the
distributed result and its cleanup finish. Exiting/reentrant warning
handlers can safely use the pool. If that invocation fails first, the
original error remains primary and retains those warnings in its
.npRmpi.deferred.warnings attribute. Immediate design
warnings, arbitrary callbacks/manual broadcasts and rank-asymmetric
warning-to-error conversion are not covered by this repair.
Bounded covariance Gaussian calibration now generates one maximum sample for all requested confidence levels, preserving normal-stream continuation, including a pending Box-Muller deviate. General tiled and callback replay remain separate contracts.
Add experimental plot-only corrected density intervals for fixed, known-bound order-2 beta kernels in one or two continuous dimensions: asymptotic, symmetric-sd and asymmetric-sd, with explicit pointwise/simultaneous bands. Share the guide/residual preparation, portable native arithmetic and bounded calibration storage. Range-derived bounds and literal endpoints are rejected. Core estimators, bandwidth searches, estimator SEs, se=FALSE and incumbent Gaussian/D8/MP1 constructions are unchanged.
Document that autodispatch.option.sync="never"
requires caller-maintained computational-option agreement across ranks.
This is an existing limitation; runtime behavior and communication are
unchanged.
MPI task sequencing now follows the live session independently of user options() restoration.
Asymptotic unconditional density plots mark empty-window uncertainty unavailable while retaining estimates and the full evaluation grid.
MPI option-agreement bookkeeping now remains valid when user code restores an earlier options() snapshot.
Density and distribution hat operators require numeric RHS storage, without coercing factor codes or numeric-looking strings.
Native default bandwidth-constructor and kernel-sum calls reject formula-only subset and NA-action arguments instead of ignoring them. This guard does not certify the argument policy of every stored-bandwidth or testing route.
Preserve the declared regression-fit owner in private partially linear MPI calls, including namespace-only and masked-package use.
Extend estimate-centered density asymptotic joint variability bands to fixed Gaussian and Epanechnikov orders 4, 6 and 8 in one/two continuous dimensions with unbounded or finite known fixed support. Signed kernel contributions, existing marginal intervals and estimators are preserved.
Corrected Gaussian regression now refuses unsafe uncached Box-Muller replay at the resolved-data cache boundary, including legacy and NA-omission routes that cannot be classified before plotting. Data/NA callbacks are not repeated; cached and other-generator paths retain their existing behavior.
MPI regression degree search rejects an already-decoded nonfinite initial bandwidth before starting its prepared worker command, preserving the pool on the constant-regressor case. Native solver and manual-command handling are unchanged.
Native-vector MPI fits reconcile descriptive variable identities before publication, including retained training/index names and nested fit copies. Explicit data-frame names, executable calls and acknowledged input leases retain their existing ownership.
MPI auto-dispatch now includes R’s warn option in
the existing synchronized option policy, keeping warning conversion
consistent at symmetric warning sites. This does not alter
exiting-handler or interrupted-collective recovery.
Preserve native-vector column identity when unconditional density/distribution fits retain training data and plots reconstruct their grids; explicitly named frames and matrices keep their own names.
Clarify the aggregate D8 inference-storage forecast and its pool-size limitation.
Session help now states the existing worker-loop limitation for errors escaping manual broadcasts; master-only error handling does not restore a failed worker pool. Runtime handling is unchanged.
Regression prediction and residuals now retain the existing collective evaluation path on all ranks during manual broadcast.
MPI calls now retain their established package owner when expanding dots, including namespace-only bandwidth-constructor aliases.
D8 density plot fanout now preserves the caller’s RNG state around transport-tag generation, without changing innovations or interval values.
Initialize an available user-supplied normal generator for geometric-bootstrap worker tasks, which can draw normals indirectly. The existing preflight rejects a missing worker generator before draws; uniform-only tasks are unchanged.
Reject structurally degenerate full-sample fixed blocks in corrected density inference instead of returning zero-width uncertainty bands.
Preserve non-syntactic retained training-column names in corrected CDF plot recovery. Explicit mismatched frames remain rejected.
Label smooth-coefficient plot targets with the selected coefficient, including the intercept, without changing their values.
Correct the panel-specific bias-corrected curve drawn in common-scale CDF slice plots. Returned estimates and interval endpoints are unchanged.
Preserve the original partially linear preparation error on the MPI broadcast source, allowing the existing coordinated error path and session reuse.
Retain effective replacement training frames when restoring formula metadata after MPI regression fits, while preserving constructor and callback ownership checks. Estimator calculations and lease/transport policies are unchanged.
Copula displays tolerate bounded floating-point rounding in computed values at exactly duplicate coordinates, using the center and error-offset scale when interval endpoints nearly cancel. Conflicting values and distinct coordinates still fail; fitted values and interval calculations are unchanged.
MPI bandwidth fingerprints now account for R’s serialization of evaluated arguments captured by local NA-policy functions. Callback objects, lexical ownership, mutation checks and the lease protocol are preserved.
Single-index MPI reentry preserves integer-response types and names until the arithmetic stage, avoiding false retained-training-metadata conflicts.
Conditional inference preserves coordinator statistical RNG advancement across deterministic MPI transport, so later panels follow serial draw order.
Document all bias-corrected intervals and bands as experimental (pre-beta research), incomplete and not recommended for general-purpose inference. This includes asymptotic, bootstrap and legacy constructions; favorable bounded evidence is not a general coverage guarantee. Calculations, defaults and returned objects are unchanged.
Consolidate plot-only return values across estimator families
into np_plot_data. output="data" and
output="plot-data" return the same computed geometry: named
panels and targets, absolute interval endpoints, compact inference
metadata and explicit finite coverage families. Use
as.data.frame(result, panel=..., target=..., band=...) for
a tidy target. Plot objects no longer masquerade as fitted estimators or
expose overloaded error offsets, bootstrap draws, covariance workspaces
or fitted objects. plot(result) replays the retained
geometry without refitting or resampling.
Compound plot arguments now use snake_case
(bias_control, boot_control,
gaussian_draws, random_seed,
gradient_order, etc.). Retired plot spellings and
output="both" fail explicitly. Estimator APIs, control
constructor arguments, numerical methods and defaults are unchanged.
Package examples, tests and demos follow the new plot contract.
Coefficient displays retain their existing geometry: partially linear coefficient bars represent one standard error, and smooth-coefficient coefficient curves have no confidence bands. Explicit incompatible inference requests now fail clearly instead of being ignored.
Add an experimental asymmetric jackknife-MP1 conditional density
bootstrap through
np_conditional_bias_control("jackknife-mp1"). Complete
corrected roots use per-resample mixture studentizers, signed inversion,
a marginal safeguard and companion hulls. This explicit
conditional-pilot opt-in leaves existing analytic SD and asymptotic
jackknife methods and defaults unchanged. Symmetric MP1 and the original
full-t research candidates remain private.
Bias-corrected asymptotic Gaussian density plots now use a complete D8 positive-pilot covariance and full-grid maximum calibration for eligible continuous data. Explicit density bias controls and bootstrap constructions retain their meanings.
Add six experimental opt-in LC conditional density/CDF inference
routes via np_conditional_bias_control(): corrected
asymptotic bands and explicit bootstrap="conditional-pilot"
symmetric/asymmetric SD bands. Existing methods/defaults are unchanged.
Density asymptotic uses jackknife correction; density bootstrap and all
three CDF routes use analytic correction. Plot inference records
absolute endpoints and finite-family scope. Initial scalar
Gaussian2/fixed-bandwidth and numerical workspace limits, conservative
subfamilies and small-sample coverage failures are documented.
Help now explains the current distributed custom-uniform RNG replay refusal, explicit built-in-generator selection, and the separate npsigtest limitation. This is documentation only: it does not change RNG admission or claim to restore preloaded custom-generator support.
Density/CDF outer selection rejects replay arguments inconsistent
with retained kernel, scaling and manual-bandwidth metadata. Explicit
manual selection retains its replay rule when
eval.only = TRUE.
Leased regression, index, smooth-coefficient, partially linear and quantile bandwidths finalize public names and formula metadata before publication. Native/formula fits retain supported worker-store reuse; local formula lookup and rejection of mutated or serialized stale handles are preserved.
Coordinated command and argument errors retain their original classes, calls and attributes through evaluation and acknowledgement. This does not change the timing of warning delivery or the existing error/interrupt precedence.
Automatic dispatch retains the package method already selected on entry when materializing a generic-looking call. A rank-local same-name S3 method can no longer trigger a second, inconsistent dispatch of that transported call. Initial public method selection and formula lookup are unchanged.
A computation error now remains primary when an MPI command also receives a deferred interrupt. The interrupt is retained as secondary condition evidence, and failed publication rollback completes before control returns to the caller.
Scheduled object broadcasts, scatter, remote execution and parallel-simulation helpers prepare their payloads before asking workers to receive them. Local serialization failures therefore leave peers available for subsequent work. The scatter-to-slaves wrapper also resolves its package-owned receiver explicitly.
Native-vector unconditional density and distribution results now finalize generated variable labels consistently in retained training data and bandwidth selection metadata. Corrected inference no longer rejects these otherwise valid fits because of internal transport names. Supplied column names and external bandwidth provenance are preserved.
MPI hat-matrix broadcasts keep internal temporaries local to each invocation. Failed publication rollback now preserves the primary error and avoids another cleanup command after the session has become unsafe to reuse.
Normal-consuming distributed calibration checks that a selected custom normal generator is loaded on every rank, then initializes that generator before task replay. Built-in generators and uniform-only paths keep their existing streams.
Fitted regression plots reject retained categorical-response provenance before reconstructing outcomes from fitted values and residuals. Conditional plots validate supplied training types against bandwidth metadata before grid creation. Valid numeric outcomes and categorical predictors retain their existing behavior.
Multi-variable np.pairs() validates the
numeric-response requirement before starting fits; single-variable
density panels remain available for factors. Explicit bandwidth values
are passed once to each panel constructor and no longer conflict with
the fitted bandwidth object during MPI estimation.
Distributed bootstrap routes that replay raw RNG states now reject an effective user-supplied uniform generator before advancing the draw bank. Built-in substreams, precomputed/deterministic fanout and explicit supported local execution retain their existing behavior; no generator is substituted.
Density/CDF smoothed-percentile ranks retain the original nominal tail probability, avoiding an extra subtraction from one at integer rank boundaries (for example alpha=0.1). This can narrow or widen affected nondefault-alpha companion bands; the default alpha=0.05 construction is unchanged.
Calibrated smoothed-percentile resolution diagnostics now report the empirical indices actually used after companion enlargement. Regression rank details retain point-specific indices and the actual included-curve count. Endpoints, calibration selections, resampling and RNG behavior are unchanged. The aggregate limited-resolution warning can consequently list different affected bands.
Package-generated MPI commands keep their temporary bindings in private child frames, preserving worker global objects and explicit user broadcasts.
Retire acknowledged autodispatch publications when an interrupt or a returned-object assembly error prevents transfer to the caller. Preserve the original condition and poison lease ownership if rollback fails.
Public regression, single-index, smooth-coefficient and partial-linear plots enforce the numeric-response contract before coercion or NA indexing. Klein-Spady plots retain the numeric 0/1 requirement.
Legacy density/CDF smoothed-percentile outer fits use their generated data directly, including transformed formula terms, without looking up the original variables in the global environment.
Plain npcopula plots now preserve an omitted
center argument instead of incorrectly requiring bootstrap
errors. Explicit control validation is unchanged.
Ordinary bootstrap fan-out now restores remote workers’ entry random seed and generator kind after each task, including errors and interrupts, while preserving coordinator stream ownership and the bootstrap draws.
Density/CDF smoothed-percentile outer fits now repeat retained bandwidth selection rules; new bandwidth objects retain value-only provenance. Older CV objects without a complete recipe retain explicitly warned compatibility behavior. Stopped assessment status is now accurately described.
Corrected bootstrap plotting now completes its target lifecycle and reports an evolving ETA for the 39 outer calibration samples. Numerical results, RNG state, and computational ownership are unchanged.
np_boot_control(calibration = "legacy") selects the
retained legacy bias-corrected bootstrap construction for regression,
density, and CDFs. It is an explicit compatibility option; existing
defaults are unchanged.
Regression smoothed-percentile capacity is checked before corrected-row capture using the existing memory forecast and limit. Documentation now distinguishes companion bands and support completion, and records the unresolved nondefault-selection and regression coverage limitations.
Small MPI object exchanges no longer force a full R garbage collection after every all-gather. Payloads, rank order and numerical computation are unchanged.
npreg, npplreg, npscoef,
and npindex, including their bandwidth selectors, now
require numeric integer/double primary responses and explicitly selected
evaluation responses. Formerly accepted factor and ordered responses
error. Categorical predictors and internal auxiliary response encodings
are preserved. Extracted categorical npplreg child
bandwidths also require an explicitly numeric response when used through
public npreg or npregbw. Klein–Spady retains
its numeric 0/1 rule. Unusable automatically discovered single-index
scoring outcomes leave predictions available without scores.
Autodispatch arguments and temporary service contexts now live in package-owned environments, preserving caller lookup and explicit user broadcasts without creating temporary bindings in the global workspace.
Serialized bandwidth objects remain usable through normal argument transport; only their original in-memory handles can reuse a session lease.
Single-index bandwidth reentry retains native training data before publication, allowing its unchanged result to reuse a lease in subsequent fitting calls.
Regression smoothed-percentile calibration now carries explicit RNG state with each outer task, retaining the selected generator and sampling kind when workers have different RNG settings. Calibration seeds and statistical calculations are unchanged. A user-supplied uniform RNG is rejected before distributed preparation; serial and explicitly supported local execution retain their existing route.
Require every started smooth-coefficient search worker to acknowledge service shutdown before context retirement or the next collective. Uncertain service cleanup prevents pool reuse and preserves the original computation condition. Session shutdown now checks affirmative teardown results before resetting state; cleanup diagnostics cannot prevent the terminal teardown attempt.
Add experimental CDF and regression
smoothed-percentile opt-ins through
np_boot_control(). CDF retains the univariate corrected
smoothed-bootstrap prototype; regression exposes calibrated unshifted
standardized-residual percentiles for LC/LL/Bernstein LP means and first
derivatives. Regression uses the explicit residual law, not wild/Mammen.
Existing defaults and SD constructions remain unchanged; supported
scopes and coverage limits are documented. Exact partial selection and
bounded outer geometry reuse retain the private regression candidate’s
arithmetic and RNG.
Density smoothed-percentile plot subtitles and legends now identify corrected bootstrap curves and calibrated percentile ranks, without changing calculations, returned objects or the existing SD-method labels.
Bias-corrected bootstrap calibration uses common
symmetric-sd and asymmetric-sd names across
regression, unconditional density and CDFs, preserving existing defaults
and older public names. CDF asymmetric fixed-bandwidth inid/fixed/geom
support and low-draw diagnostics are included.
Density smoothed-percentile selects the refined
calibrated unshifted OP1+SC1 construction with multivariate/mixed-data,
finite categorical near-pooling and ordered-lattice extensions. The
original fit is unchanged; 39 fresh outer samples reselect CV bandwidths
for calibration. This opt-in method uses direct corrected-curve ranks,
companion hulls and certified support completion, with explicit
resolution/attainability diagnostics. It requires ks, fixed Gaussian2
bandwidths, at least one numeric coordinate, and inid or unit-block
resampling. CDF/regression percentile adaptation and nonunit-block/NN
calibration remain future work. The private centered experiment is
preserved in research artifacts, not selected by this name. Gaussian q1
paired evidence and broader capability proof are distinguished from
still-limited multivariate/mixed coverage evidence.
Defined corrected density reference/joint-scale bootstrap bands now return all requested finite bands at low draw counts, including Bonferroni, with one aggregate warning and location/scale resolution diagnostics.
Bias-corrected regression bootstrap bands now offer raw
asymmetric np_boot_control(calibration="separate-tails")
and conservative calibration="tail-envelope" choices for
wild, inid, fixed and geom. Existing method defaults and Mammen weights
are preserved. Corrected fixed and geom empirical bands use a
pilot-anchored selected-block scale; pooled studentization and
normal-scale intervals retain their existing behavior. Finite-sample
undercoverage and localized width limitations remain
documented.
Defined regression empirical bands are returned at low draw counts with one aggregate resolution warning and saved diagnostics, including Bonferroni. Genuine undefined-variance and invalid-input errors remain distinct.
Corrected unconditional density and distribution plots now accept
the default band="pmzsd", using the corrected scale and the
normal critical value determined by alpha. Explicit
calibrated bands and legacy routes retain their existing
behavior.
Extend fitted-row bias correction to continuous multivariate fixed-bandwidth Gaussian/Mammen means and first derivatives with inactive local-to-global adaptation, including accepted regularized raw and Bernstein main fits. One capture per fit supplies the complete correction and covariance; the existing multivariate variance and studentization rules remain unchanged. Full-surface qualification retains sparse-region width and marginal-coverage limitations. No new nearest-neighbor, mixed-data or higher-derivative regularized-row admission is implied.
Improve numerical precision of higher-order P1 bias-pilot derivatives by differentiating the same weighted polynomial system in orthonormal coordinates. Main fits, rank decisions, pilot settings, variance rules and the default 16 correction iterations are unchanged.
Private distribution plots can select absolute-process bias-corrected bands for fixed Gaussian kernels with one or two numeric variables. Explicit studentized and legacy constructions remain available; qualification is ongoing.
Extend the fixed-bandwidth univariate Gaussian/Mammen bias-corrected plot construction to raw main polynomial bases, for means and first derivatives, using the original fitted rows and the existing variance calibration.
Allow bias-corrected Gaussian and Mammen plots to use the original fitted rows for univariate continuous fixed-bandwidth means and first derivatives with inactive local-to-global adaptation, including qualified regularized Bernstein fits. Pooled residual normalization and a positive variance pilot improve endpoint calibration. Some Mammen pointwise undercoverage remains documented; existing mixed, NN, adapted and higher-derivative routes retain their constructions.
Density plots with center="bias-corrected" now
select joint-scale intervals without a separate control argument.
Gaussian asymptotic and literal inid bootstrap calibration support fixed
second-order Gaussian bandwidths with one or two numeric variables over
the full sample range. Returned corrected centers plus signed error
offsets reconstruct both endpoints for curves and surfaces. Coverage can
be conservative; small bivariate samples can have wide bands and weak
individual locations. Explicit positive-covariance and
legacy selections retain their earlier constructions.
Unsupported routes and underresolved bootstrap Bonferroni requests fail
clearly.
Plot argument validation now accepts explicit
center="estimate" with errors="asymptotic",
including density and copula plots. Actual bootstrap controls still
require errors="bootstrap".
Add opt-in fixed-bandwidth unconditional density bands via
np_udens_bias_control(). The positive-covariance
construction corrects numeric and categorical smoothing contributions
and uses literal inid absolute studentized calibration. Existing
defaults and legacy routes are unchanged when this construction is
selected explicitly. Its Gaussian, NN and dependence extensions remain
unqualified; the positive-covariance construction has an explicit
bounded-grid and memory contract.
Stabilize continuous-only local-to-global bias correction near activation using nested weighted polynomial projections. Preserve the main fit, 16-step correction, existing calibration laws and mixed-data M1 construction. Bounded paired coverage confirmation includes means, first derivatives and a cubic second-derivative slice; near-singular geometry remains unqualified.
Reuse immutable auxiliary geometry and exact single-anchor calculations without changing corrected rows, covariance or calibration. Finite-grid coverage remains broadly comparable to the preceding construction. Bounded qualification accepts small active-transition median costs and specified timing-tail exceptions.
Pooled geometric-block bias-corrected bounds now use their own exact finite-sample circular covariance and replicate score studentization. Literal stationary-block draws and raw-residual block selection remain unchanged. Separate bounded confirmation supports regular cases with the documented GNN, nominal-level and dependent-data limitations.
Pooled fixed-block bias-corrected bounds use the exact finite-sample circular-block score covariance and replicate-specific score studentization. Raw-residual block selection, literal draws and fixed model are preserved. Regular 95% coverage improved in bounded confirmation; weak GNN means, interval-width costs and limited dependent-data evidence remain documented.
Pooled bias-corrected Rademacher bounds now use the residual-pilot studentizer with literal plus/minus-one innovations. The fixed model and observed standard errors remain unchanged. Bounded confirmation supports regular fixed/NN cases, with an explicitly documented weaker 90% mean cell; existing nonpooled Rademacher and mixed M1 calibration are unchanged.
Map ordered-factor floor rugs to the same numeric coordinates as their surfaces in base and rgl plots, preserving numeric and nonnumeric level spacing. This prevents calendar-year rugs from distorting rgl scenes.
Keep automatic Gaussian band subtitles visible on ordinary plot devices using compact measured labels, including bias-corrected simultaneous bands. Preserve explicit subtitles, font size and plotting margins.
Integrate the frozen M1 local-to-global correction for eligible mixed-data fixed Gaussian regression bands with Gaussian or Mammen calibration. Adapt P0 residuals and complete observation rows without changing the main model, variance-smoothing bandwidth or default 16 iterations. Preserve adaptation=“none”, inactive geometry, other calibration laws and continuous adaptation. Categorical contrasts use both endpoints from one captured fit. M1’s narrower bands carry the documented target-specific coverage trade-offs.
Extend the existing geom calibration to higher continuous derivatives on the original continuous and simple mixed-data routes. Preserve its literal resampling law, corrected rows and defaults; pooled admission is unchanged. Admit the same literal law for finite-profile categorical-only inference, with its documented small-profile coverage limitations.
Extend the existing fixed calibration to higher continuous derivatives on the original continuous and simple mixed-data routes. Preserve its literal resampling law, corrected rows and defaults; pooled admission is unchanged. Admit the same literal law for finite-profile categorical-only inference, with its documented small-profile coverage limitations.
Extend the existing rademacher calibration to higher continuous derivatives on the original continuous and simple mixed-data routes. Preserve its literal resampling law, corrected rows and defaults; pooled admission is unchanged. Admit the same literal law for finite-profile categorical-only inference, with its documented small-profile coverage limitations.
Admit the existing inid corrected-score calibration for pooled means, derivatives and categorical contrasts, and for original higher derivatives. Retain the complete correction, smoothed variance and all frozen defaults. Other pooled bootstrap laws remain unqualified. Admit the same literal law for finite-profile categorical-only inference, with its documented small-profile coverage limitations.
Expand the bounded pooled auxiliary workspace from 256 to 384 columns. Retain the same rank and residual-support checks, with preallocation guards and bounded row storage. Existing smaller spaces keep identical arithmetic. This capacity extension does not resolve sparse weighted-pilot identification.
Extend pooled mixed-data Gaussian/Mammen correction to regular generalized- neighbor main fits, using the existing fitted-row and derivative conventions. Preserve fixed neighbor counts and auxiliary geometry; positive-ridge pooled rows remain unsupported and tiny-neighborhood stress limitations are documented.
Extend pooled mixed-data Gaussian/Mammen correction to regular adaptive-neighbor main fits, including admitted pure derivatives and categorical contrasts. Preserve selected neighbor counts, degree, auxiliary settings and the existing fixed-bandwidth construction. Unidentified or regularized pooled rows still fail.
Add iterated bias-corrected means and categorical contrasts for fixed-bandwidth categorical-only regression. Use the exact observed-profile reduction, full contrast covariance and Gaussian/Mammen studentization; retain the main fit and 16 requested iterations. Unidentified profiles and inapplicable continuous auxiliary controls fail explicitly. Existing continuous routes are unchanged.
Clarify that iterated Gaussian/Mammen pointwise, simultaneous and Bonferroni bands use studentized empirical quantiles; pmzsd keeps its normal-quantile interpretation. Document finite-draw resolution of Bonferroni tails.
Automatic bias-corrected categorical panels use observed values while keeping declared levels and the fitted categorical kernel intact. Missing contrast reference/adjacent endpoints remain explicit identification errors.
Regression inference compares retained model state independently of historical call metadata. This restores formula-based MPI local-to-global correction while preserving exact data, bandwidth, degree and evaluation identity.
Corrected-process MPI calibration now transports its factor matrix safely for a single resampling chunk. Literal laws, draw streams and distributed ownership are unchanged; small explicit draw counts no longer collide with R argument matching.
Fixed-bandwidth pooled categorical bias correction now supports continuous pure derivatives and categorical first differences with Gaussian/Mammen calibration. Complete pilot derivatives and endpoint cross-covariance use the existing 16-iteration construction. The qualified bounded auxiliary workspace expands from 128 to 256 columns without changing rank or residual support requirements. Existing mean and simple-profile owners are retained.
Bias-corrected regression plots support higher pure coordinate derivatives with Gaussian or Mammen bands when the fitted polynomial has at least one degree beyond the requested derivative order. Existing mixed-degree and gradient.order vectors retain their coordinate-specific meaning.
Extend iterated bias-corrected fixed-bandwidth regression means to additional categorical layouts using a shared-curvature/profile-main auxiliary and full covariance. Gaussian and Mammen calibration retain 16 default iterations and existing controls. Previously admitted routes are unchanged; sparse pilot rank failures remain explicit. Document the bounded coverage evidence and unsupported pooled NN/additional-bootstrap extensions.
Calibrate iterated bias-corrected Gaussian and Mammen regression bands using replicate-specific residual-pilot studentization. Preserve the corrected center, observed SE, 16 iterations and selected model. Record the construction in returned inference metadata and describe empirical Gaussian marginal quantiles accurately in plot labels. Other bootstrap laws remain unchanged.
Activate coordinatewise local-to-global bias correction for
eligible fixed Gaussian regression plots, reusing the actual fitted
observation rows and adapted P0 residuals. Preserve frozen settings,
bounded storage, and ordinary categorical/NN construction. Add
np_bias_control(adaptation=) with explicit
reference/override semantics and return effective adaptation
settings.
Improve narrow-bandwidth local-polynomial fitting, gradient and covariance precision using the accepted ridge with stable QR evaluation and row replay. Bandwidth/degree search objectives and ridge policy are unchanged.
Regression Gaussian simultaneous variability bands now reuse the distributed fit’s accepted influence rows and residuals, including coordinatewise large-bandwidth decisions. MPI fitting remains rank-owned; ordinary fits, bootstrap bands and bias-correction settings are unchanged.
Stream exact marginal calibration summaries and common-draw family maxima instead of retaining the complete plot-grid-by-draw matrix.
Admit regularized generalized-neighbor Bernstein main rows for iterated correction using their full canonical operator and correction covariance; retain auxiliary-rank checks and return main regularization diagnostics. This extension currently admits Gaussian calibration only, with documented Gaussian-error validation limits; bootstrap calibration remains unqualified for regularized main rows and is explicitly refused without changing laws.
Bias-corrected regression inference removes an unused HC3 leverage refusal, clarifies auxiliary-support diagnostics, and documents fixed-factor wild calibration, profile borrowing and computational limitations.
Generalize iterated regression bias correction to arbitrary positive continuous dimension and package-admissible mixed GLP main degrees. Add automatic and scalar/vector auxiliary degree controls, preserving literal overrides and recording the resolved polynomial space. Preserve fixed model specifications and bounded observation-row storage.
Honor explicit xlim in continuous regression plot
panels, including derivative and common-scale panels, without changing
the inference grid.
Add np_bias_control() and iterated
npreg plot inference through
center="bias-corrected", with Gaussian and bootstrap
calibration of the complete corrected observation operator. Preserve
selected bandwidths, degree and basis; use stable auxiliary Bernstein
bases above degree one, scale-equivariant pilot rules and bounded
training-row storage. Return effective settings and inference
diagnostics. The initial supported scope and coverage qualifications are
documented in ?np_bias_control.
Make Mammen the default wild multiplier throughout plotting.
Explicit Rademacher remains available.
np_bias_control(construction="legacy") retains the prior
regression bootstrap correction when requested.
Reuse invocation-local training preparation for uniform
adaptive-neighbor density-copula standard errors, preserving
query-specific support faces and unavailable rows. Point estimates and
se=FALSE are unchanged.
Correct explicitly requested nearest-neighbor density-copula
pointwise standard errors to include the same-sample joint/marginal
ratio covariance. Preserve point estimates, selected bandwidths,
se=FALSE, fixed-bandwidth and distribution-copula owners,
and simultaneous-band calibration. Retain existing adaptive-neighbor
radius/support-face terms; additional generalized- neighbor radius and
estimated-coordinate uncertainty remain deferred.
Improve data= diagnostics for directly supplied,
unbound native observation names across estimator and bandwidth
interfaces, including direct default methods. Preserve formula
factories, legitimate data consumers and evaluation order; native inputs
still use ordinary R evaluation rather than a data mask.
Standalone profile/manual-broadcast jobs terminate with a nonzero status after an unhandled master or worker error, preserving the diagnostic instead of waiting indefinitely in cooperative shutdown. Caught errors inside the broadcast expression remain recoverable; session/attach behavior is unchanged.
Preserve explicit data=NULL when reusing
formula-bearing least-squares quantile bandwidth objects, and retain the
stored-sample diagnostic for prohibited replacement data. Unsupported
native data remains an error.
Clarify the partially linear formula-bandwidth plot route’s
data support.
Use shared MPI test lifecycle helpers for the focused formula/native-input contracts; computational checks skip cleanly when a pool is unavailable.
Native data= rejection also handles arguments
forwarded through wrapper ellipses without forcing their
values.
Native methods now reject unsupported data,
including explicit data=NULL, with guidance to use formula
calls, explicit inputs or with(data, ...). Formula refits
and genuine native data consumers retain their interfaces.
Plot routes that do not consume replacement data now reject
it, while the supported partially linear formula-bandwidth route retains
that input. The IV native calls also diagnose bare unbound response
names accompanied by z, w and
data without evaluating that data argument.
Update the getting-started guide to describe supported
simultaneous asymptotic bands and the required released crs
dependency.
Conditional hat-operator row fanout now establishes the worker-local native computation mode as well as the R dispatch guard. This prevents cross-rank mixing for beta-kernel derivative apply requests while preserving the outer row partition, caller state and error restoration.
Asymptotic simultaneous plots now report unavailable joint bands when compact-support degeneracy leaves their correlation or marginal uncertainty unidentified, preserving available pointwise and Bonferroni bands. Projected class probabilities no longer acquire zero-width simultaneous bands solely because clipping or probability projection has flattened their local derivative.
Clarify the conditional-response working covariance used by quantile and class-probability bands and its limitations at fixed nonshrinking bandwidths.
Copula plots with repeated grid coordinates now draw asymptotic simultaneous bands consistently, retaining every requested output row. Identical targets share one calibrated band; conflicting duplicates are still rejected.
Use portable explicit dependencies for conditional-kernel source fragments, preserving incremental rebuilds without GNU Makefile extensions. Declare the private conditional transport dependency for static R-code checks and update the SSHRC funding URL. Estimator computations are unchanged.
Detect the modern MPI info-string API with explicit Open MPI prefixes as well as pkg-config, without requiring unrelated MPI handle data symbols.
Use MPI_Info_get_string when the MPI library
provides it, avoiding the deprecated-call installation warning with
newer Open MPI. Older MPI and Windows builds retain the legacy API. The
replacement preserves requested string truncation; unlike the legacy
MPICH call, a short buffer need not raise an error.
Conditional density, distribution, quantile and class-probability fitting and replay now use the same literal categorical kernels on both X and Y. Near-upper categorical bandwidths no longer select an approximate constant target for some requests. Direct regression and unconditional kernel-sum behavior and caller options are preserved. Older conmode objects retain stored-value extraction; reconstructing uncertainty around historical stored values requires an explicit refit.
Document numerical-conditioning limitations of raw-basis conditional local-polynomial fits. The Italy examples now centre calendar years and apply the same offset to prediction data; estimator arithmetic, defaults and conditioning checks are unchanged.
Copula distribution plots with only ordered margins now construct adaptive nearest-neighbor simultaneous bands using their finite-support CDF factors.
Class-probability surfaces reject repeated conditioning-variable selectors before evaluating or drawing a misaligned grid.
Explicit scalar-quantile perspective requests, including least-squares quantile objects, now draw their requested surfaces. Default slice plots, vector-quantile and gradient layouts retain their existing behavior.
Class-probability surfaces draw their available asymptotic intervals. Copula surfaces preserve statistical rows while mapping identical display coordinates; conflicting duplicates are rejected rather than averaged.
Nearest-neighbor copula density bands include shared-observation covariance between the numerator and both estimated marginals, conditional on the fitted smoothing state and fixed inverse coordinates. This does not include uncertainty from estimating the inverse-coordinate grid.
Data-only regression plots preserve the caller’s graphics state. Rendered regression plots retain their existing cleanup behavior.
Least-squares quantile objects diagnose duplicate quantile levels before plot preparation, retaining the existing rejection contract.
Correct single-index gradient plots with asymmetric bootstrap bands when an index coefficient is negative: lower and upper widths now exchange roles before scaling, including all-band data and rendering.
Preserve the caller’s graphics state during data-only density, distribution, conditional, single-index, smooth-coefficient and partially linear plots. Rendered plots retain their existing cleanup behavior.
Class-probability gradient plots now render the legacy
band="pmzsd" pointwise alias with the correct
palette.
Conditional-quantile plots reject unsupported derivative orders instead of labelling first derivatives as higher derivatives. First derivatives, LSQ and conditional-distribution derivative contracts are unchanged.
Quantile, least-squares quantile, class-probability and copula plots now offer Gaussian simultaneous variability bands over each requested finite panel grid. Supported local-constant/local-polynomial, continuous-gradient, categorical-effect and nearest-neighbor routes follow each estimator’s existing API. Joint inference retains shared-observation covariance and reports unavailable limits for nonregular or uncertified configurations. These are variability bands conditional on the documented fitted smoothing state, not smoothing-bias-corrected population confidence bands. Existing pointwise and Bonferroni contracts are retained.
Preserve the accepted conditional-CDF arithmetic for all-large ridge maps and bounded beta-kernel deleted fits. Response translation is now confined to constant-reproducing, numerically suitable conditional owners; the estimator’s existing objective, solve and fixed-regression paths are unchanged.
Stabilize conditional local-polynomial distribution fits for saturated responses without changing the basis or accepted solver. Conditional categorical-response gradient uncertainty and simultaneous plots now use the fitted operator’s effective category weights, including exact zero uncertainty for a constant effective response.
Conditional LC density/distribution gradient bands now reconstruct unordered explanatory-factor weights from the estimator’s effective native kernel owner, including weights near their upper bound. Fitted gradients, marginal standard errors, bandwidth selection and other plot routes are unchanged.
Density and distribution plots now offer Gaussian simultaneous variability bands for supported fixed and nearest-neighbor specifications, conditional LP/gradient panels, and level surfaces. Existing marginal standard errors are retained; Gaussian simulation uses 9,999 draws by default. Proper projections retain their previous marginal uncertainty contracts; the additional four-family joint bands are described above.
Correct conditional density/distribution Bonferroni surface multiplicity to use the full evaluation grid; previously a vector row count reduced it to one.
Partially linear asymptotic plots warn when the fitted curve is numerically unstable at evaluation points. Their marginal limits are NA at those points, and simultaneous limits are NA for the affected panel; other points and panels retain their available uncertainty. The fitted curve is preserved.
Partially linear LC plot influence blocks now run locally, avoiding repeated MPI dispatch while preserving the computed intervals.
Partially linear asymptotic plots reuse their nuisance fits’ accepted local factorization for influence calculations, preserving fitted values and ridge policy. Reconstruction is checked on the plotted SE scale. Training work is reused within a plot and consumed in bounded blocks, including nearest-neighbor geometry. Pointwise, simultaneous and Bonferroni bands share the corrected SEs.
Simulated Gaussian critical values are bounded by the analytical pointwise and Bonferroni critical values, preserving band nesting despite Monte Carlo variation. Calibration metadata retains the raw simulated critical value.
Unavailable simultaneous-only plot limits retain their NA rows, aligned with retained fitted centers. Smooth-coefficient and partially linear displays retain a finite axis range when the requested joint limits are unavailable.
Regression, single-index and smooth-coefficient joint-band
reconstruction, residual-information and workspace limitations now
produce a warning and NA simultaneous limits; band="all"
retains its pointwise and Bonferroni bands. Invalid arguments and
unexpected errors still fail explicitly. The estimator, ridge policy and
reconstruction tolerances are unchanged.
Plot bootstrap defaults previously set to B=1999 are
now B=9999, including copula plots. Class-probability plots
retain B=399. Gaussian simultaneous calibration defaults to
9,999 draws. Bootstrap warnings distinguish bare tail resolution from
endpoint precision; Gaussian warnings use an alpha-dependent Monte Carlo
precision target for the maximum probability. Explicit counts remain
unchanged, and estimator and hypothesis-test bootstrap defaults are
unaffected.
Gaussian simultaneous plot intervals now include unordered and ordered categorical panels for regression, smooth-coefficient and partially linear means, and the existing supported gradients. Regression categorical gradients retain endpoint cross-covariances and the established reference or adjacent-level contrast convention. All-categorical local-constant smoothing is included. Existing categorical contrast definitions and bootstrap constructions are preserved.
Gaussian simultaneous plot bands also support generalized and adaptive nearest-neighbor bandwidths. Training residual normalization preserves the estimator’s training-radius convention. Partially linear mean curves and surfaces now use a complete response influence map, including coefficient estimation and all shared-observation cross-covariances. This corrects their pointwise and Bonferroni plot SEs as well; fitted values, coefficient vcov and bootstrap procedures are unchanged. The additional computation is confined to requested asymptotic plot uncertainty.
Asymptotic Gaussian simultaneous bands now include supported
regression, single-index and smooth-coefficient gradients, and
regression and smooth-coefficient mean surfaces in perspective and rgl
plots. Surface calibration uses observation influence factors without a
square grid covariance matrix. gaussian.draws uses a fixed
draw count. The fixed one-million draw ceiling has been removed;
allocation checks remain. Gaussian simulation reports draw progress and
elapsed time using the bootstrap heartbeat mechanism.
Plot joint-band admission now honors the existing legacy argument abbreviations consistently with full method and band names.
Regression, single-index and smooth-coefficient mean plots now
offer asymptotic Gaussian simultaneous variability bands over each
panel’s finite grid for fixed-bandwidth LC/LL/LP fits with second-order
Gaussian or Epanechnikov kernels. gaussian.draws controls
the Gaussian calibration separately from bootstrap B; joint
requests report unavailable residual information explicitly.
Single-index bands condition on the fitted index coefficients;
smooth-coefficient bands use the noniterated mean and accepted raw
residuals, with explicit reconstruction checks. Joint calibration uses
the coordinator in an active session/attach pool; manual-broadcast
requests fail explicitly.
predict() for location-scale quantile fits now
accepts named tau to select stored quantiles in the
requested order, with aligned standard errors and training scores.
Omitted or NULL tau retains the original behavior; unfitted
quantiles fail clearly without bandwidth selection or
recalibration.
The standalone npregiv and Engel95
demos keep their MPI workers alive until the demo finishes and close
them on exit.
Local-smoothing quantile refits with explicit data and a new subset now use the requested rows when the starting regression bandwidth also had a subset. Omitting a new subset continues to replay the original selection.
Formula subset provenance now survives bandwidth reuse,
serialization, and forwarding through lexical callbacks. Single-index
evaluation records rows removed after na.pass or
NULL, and native vector labels support named prediction
data. Formula single-index calls no longer retain generic
frames.
Formula single-index evaluation now honors an explicit
na.action for predictor rows, including
na.exclude prediction padding and na.fail.
Omitted evaluation policies, retained training samples and native
evaluation precedence are unchanged; missing scoring outcomes do not
remove predictions.
Forwarded formula subsets retain caller-local masks and loop variables as well as data-column lookup, including single-index and local-smoothing quantile fits.
Native fits that construct bandwidths keep compact response labels rather than embedding response values in the reported variable name.
Smooth-coefficient refits using z=x report consistent smoothing-role metadata and print bandwidth summaries without spurious warnings.
Kernel documentation now describes beta support for mixed-data and local-linear/local-polynomial regression and conditional estimation.
Pooled native conditional-moment tests resolve supplied call labels on the master. Unconditional bandwidths made from a vector retain its public name for named-data prediction.
The demo CPU monitor stops and reaps its sleep process when interrupted.
Response-only native refits keep the retained design, while partial design replacements still require the other training roles. Smooth-coefficient fits using the default z=x retain that convention for prediction and refitting.
Fits that select bandwidths use the first realization of supplied search controls. Forwarded local-smoothing quantile subsets retain their data mask.
Pooled native calls resolve accepted subset/call values on the master and preserve public response names for evaluation diagnostics and plot labels.
Pooled calls preserve the master method’s already-evaluated
argument values, including random starts, search controls and
constructor metadata passed through .... Formula subset
evaluation and numerical owners are unchanged.
Partial positional training replacements no longer combine new observations with a bandwidth object’s old sample. Supply all required training roles; the smooth-coefficient default uses the newly supplied predictors for smoothing.
Formula-based local-smoothing quantile predictions process positional newdata using trained terms and predictor names, including transformed predictors.
Local-smoothing quantile subsets on regression bandwidth objects use the data mask once and accept NULL/TRUE no-op subsets. Stored LSQ bandwidth objects report an error for replacement training controls instead of ignoring them.
Explicit single-index evaluation outcomes now enforce response-scale compatibility; numeric outcomes remain valid for numeric-labelled ordered training responses.
Regression bandwidth documentation reflects beta-kernel support for local-linear/local-polynomial and mixed-predictor regression.
Materialize additional npksum arguments on the master before pooled execution, preventing worker-side name resolution failures for locally supplied values.
Match positional native training arguments before reusing retained data, while preserving positional evaluation arguments in fitted-object prediction.
Local-smoothing quantile calls evaluate supplied arguments once in their originating scope, including pooled calls and progress-wrapper re-entry. Applying a row-changing subset to a retained formula regression bandwidth requires explicit data.
Smooth-coefficient predictions preserve known training MSE even when the original fit was scored against alternative outcomes. Vector-quantile prediction reports one residual.scale value per tau.
Automatic Ichimura diagnostics do not score numeric outcomes against a model trained on factor codes. Explicit outcomes must use a compatible training-response scale.
Conditional density/distribution perspective surfaces and regression plot-data objects retain grid dimensions and trained variable names. Smooth-coefficient plot-data objects report unavailable goodness-of-fit scores as NA.
Native factor-index bandwidth objects no longer retain unrelated caller frames.
Initialize guarded native temporaries and remove unused categorical-profile bookkeeping to avoid compiler warnings without changing estimator arithmetic.
Pooled calls report a matching error from every rank without mislabelling it as an ACK mismatch, including invalid local-smoothing quantile arguments.
Local-smoothing quantile fits reuse the retained sample and bandwidth specification of formula-based regression bandwidth objects.
Partially linear plot surfaces retain predictor names and grid dimensions, including non-syntactic native predictor names, so their returned model objects support print() and summary().
Local-smoothing quantile bandwidth selection can re-enter its progress wrapper from private method calls without a function-lookup error.
Vector-quantile searches no longer strand MPI workers during progress re-entry. Their saved calls are consistent across ranks, and controls supplied as local expressions are evaluated in the originating call.
Pooled single-index LP search cleanup preserves the original error message when releasing workers, instead of substituting a generic service error.
Native search interrupt handling uses a portable R API on Windows and Unix. For interrupts observed by R, hooks and traceback run after cleanup; an explicit request to resume a released search fails with a clear error. Terminal SIGINT handled directly by the search dependency retains that dependency’s existing error behavior; it does not use this R-observed interrupt path.
Partially linear plot-data objects report unavailable goodness-of-fit scores as NA instead of shifting the training MSE into the R-squared field.
Klein-Spady automatic degree searches reject a single-class complete sample before entering NOMAD and identify class loss after omission clearly.
Automatic single-index prediction diagnostics now tolerate incompatible factor outcomes; explicit outcome requests remain strictly validated.
Prediction residual.scale no longer reuses old training MSE for positional training-data overrides. Smooth-coefficient and local-smoothing quantile predictions also report known training MSE, or NA when unavailable, without an extra fit. Local-smoothing quantile scale refers to its transformed response.
Single-index factor-design transport caches no longer escape into retained training data, and native fits no longer retain the dispatch caller frame.
Klein-Spady bandwidth selection accepts missing binary outcomes and checks that both classes remain after joint predictor/response omission. Native calls no longer reject valid incomplete samples or admit samples that lose a response class during omission.
Regression prediction with se.fit=TRUE reports available training MSE as residual.scale, preserving its historical units. Evaluation MSE is never substituted; unavailable training information is reported as NA.
Regression NN bandwidth-constructor refits also validate against the current complete sample. Predictor-only regression evaluations report unavailable goodness-of-fit scores as NA, without additional fitting work.
Contained R-observed native-search progress interrupts now restore R’s interrupt/error hooks and traceback after cleanup. Message-bearing constructed conditions retain their original identity and reporting.
Single-index prediction treats unusable automatically discovered outcomes as unavailable diagnostics while preserving strict explicit outcome validation. Differenced time-series responses align by time. Prediction residual.scale retains training MSE units and provenance, and factor-design plots preserve non-syntactic predictor names.
Fresh stored single-index and partially linear bandwidths report the common complete-case sample and omissions. Density/distribution NN bandwidth-constructor refits validate their supplied counts against the current complete-case sample.
Direct pooled single-index construction resolves factor contrasts on the master and carries that design to workers, without changing worker options.
Single-index coefficient-only refits retain their degree and bandwidth or nearest-neighbor count without re-entering automatic degree search. A supplied normalized coefficient vector is used as the first start, including zero free coefficients; single-start refits avoid unnecessary OLS initialization. A held fixed bandwidth is no longer rejected by a recomputed bandwidth-search floor. Validity checks and lower bounds for actual bandwidth searches remain.
Bandwidth-object refits now report the current training sample size after missing-row removal in regression, density, distribution, conditional density and distribution, smooth-coefficient, partially linear and single-index models. Sample-size validation uses this current count rather than the original bandwidth object’s count.
Native conditional MADS and automatic degree searches now remove jointly incomplete training rows before search setup. Regression MADS and automatic degree-search results retain the corresponding omitted-row information.
Single-index factor predictors now use retained R model-matrix contrasts in both Ichimura and Klein-Spady models. Coefficient names/dimensions, prediction, gradients and plots share that design. Numeric scores remain numeric; old saved bandwidth objects keep their original factor-score interpretation.
Klein-Spady coefficient covariance now centers covariates conditionally on the fitted index, correcting dependence of reported uncertainty on arbitrary predictor translations. Search and point-estimation formulas are unchanged; the additional moment calculation runs only when inference is requested.
Single-index fits now use evaluation outcomes automatically for
accuracy and goodness-of-fit diagnostics. Predictor-only evaluation
returns predictions with unavailable diagnostics instead of substituting
training scores. Explicit y.eval=FALSE retains labelled
training diagnostics. Conditional mode fits now use supplied outcomes
consistently with both formula and native newdata. Missing
evaluation outcomes affect scoring only, and summaries identify the
sample and number of observations scored.
Local-polynomial conditional density and distribution bandwidth searches now avoid duplicate local factorizations, omit exact zero-weight rows from their local QR work, and apply the required orthogonal factor to a single vector directly. The corrected deleted-sample criteria, rank policy and regularization are retained. This reduces search overhead without changing fitting, prediction or fixed-bandwidth regression implementations.
Beta-kernel conditional-distribution NN searches now check for cancellation between objective evaluations, including when progress display is off. This applies to local execution; distributed cancellation is unchanged.
Regression plots now use the fitted object’s retained training sample when its bandwidth object was originally constructed on different data. Native partially linear plots also preserve retained samples when recovering bandwidth state. Smooth-coefficient plots now also work when smoothing variables are omitted and the model uses its predictors for smoothing.
Extended nearest-neighbor objective evaluation now admits fractional counts consistently with their rounded integer, including counts just beyond the ordinary sample-size boundary.
Regression profile and constant-basis bootstrap plots, and
categorical partially linear fits, now propagate errors from their
selected computation instead of attempting another route. Copula plots
explicitly reject neval; set the probability grid when
constructing the copula object.
One-dimensional density plots now honor the default 50-point
continuous evaluation grid, consistently with explicit
neval. Fixed-grid conditional mode plots preserve
fractional evaluation and conditioning coordinates for numeric
predictors stored as integers. Categorical density bootstrap plots now
report profile-kernel failures instead of silently changing
computation.
Conditional CVLS with beta predictor kernels now omits the held-out observation before scaling and normalizing local-constant rows or solving local-polynomial rows. This prevents dominant self weights from erasing remaining donors and avoids cancellation when self leverage approaches one, improving density and distribution criteria near support bounds. Existing local-polynomial regularization can still produce small differences from explicit deleted-sample refits on ill-conditioned data.
Generalized-neighbor conditional-density cv.ls with
bounded uniform or Epanechnikov predictor kernels of orders 2, 4, 6 and
8 now uses the corrected deleted-sample geometry and whole-response
integral for unbounded responses. This can change selected bandwidths
and costs more than the former incorrect criterion. In retained n = 480
cases the corrected criterion took about 44–53 ms more per evaluation
(about 9 times the former cost); other platforms and workloads have
shown larger ratios, including 21–69 times the former cost in retained
reference-BLAS cases. These are case-specific measurements. Packing
gains depend on the workload and platform. Newly covered Epanechnikov
predictor orders 4, 6 and 8 took about 49–50 ms per evaluation instead
of 5–6 ms in retained n = 480 local-constant cases, with trees both off
and on. This added correctness cost is specific to the affected
bounded-X GNN CVLS route. Fixed bandwidths, default second-order kernels
and other criteria retain their existing paths.
Interrupt containment does not make all nested native allocations unwind-safe. Interrupted searches can retain allocations from shared/nested helpers; this previously deferred cleanup limitation is separate from callback containment.
Local native NOMAD objective callbacks now contain R errors and flag-based interrupts, complete native search cleanup, and then resume the original R condition or restart. This includes time limits raised inside these callbacks. Local native progress errors and interrupts now also stop the search after cleanup and re-signal the original condition object, preserving its class, message, call and custom fields (not its original restart context). This includes time limits raised during progress reporting. Message-less user interrupts retain interrupt behavior, including inside error-only handlers. R-callback degree searches retain their separate handling. Pooled searches, including rank-local native searches within a broadcast, retain their existing progress-error handling. This is not a distributed MPI timeout-cleanup repair. This repair covers local execution; recovery from asynchronous R errors during distributed searches remains a known limitation.
Native searches are not re-entrant: user condition/event handlers must not start another npRmpi search while a native npRmpi search is active.
Bounded fixed-bandwidth check-loss and Klein–Spady criteria now cancel the query-only kernel boundary mass before symmetric pair accumulation. This restores the directed leave-one-out fit for these criteria, including the scalar tree-based Klein–Spady route. CVLS/CVAIC and directed MPI arithmetic are unchanged.
A distributed interrupt can leave the current pool unable to run
later auto-dispatched searches. After control returns, close the pool
with npRmpi.quit() and reinitialize it with
npRmpi.init(). This recovery does not resolve the
separately deferred distributed timeout-cleanup hang.
Conditional-distribution CVLS with beta kernels and generalized
or adaptive nearest-neighbor bandwidths now recomputes both predictor
and response radii after deleting the held-out observation. CDF
evaluation nodes stay fixed. Bounded response tiles reuse the two
possible radii and their kernel values; wider responses use
coordinate-factor caches. This corrects objective values and can change
selected bandwidths. The additional computation is confined to these
beta NN objectives; fixed bandwidths, non-beta CDF, density and ordinary
regression routes retain their existing owners. Retained audit cases
using the default ngrid = 100 and three continuous
responses measured about 5–6.5 times the earlier per-evaluation cost at
n = 360–600; one n = 300 search cost about 5.4 times as much. These are
case-specific correctness costs, not universal multipliers. With
np.extendednn = FALSE, a deleted sample of n-1 observations
admits neighbor counts only through n-2. The separate non-beta GNN
do.full.integral geometry limitation remains.
Conditional density (CVML/CVLS) and distribution (CVLS) nearest-neighbor local-polynomial objectives with uniform or Epanechnikov predictor kernels of orders 2, 4, 6 and 8 reject trials with fewer distinct nonzero-weight deleted basis rows than basis terms. The structural check now also covers adaptive neighbors, bounded predictor kernels and mixed continuous/categorical data. It uses existing weights and the existing invalid-trial penalty; admitted trials retain their arithmetic. Higher-order kernels can therefore select different bandwidths, including without bounds. A sufficient count does not certify full rank. The one-predictor CVLS prefix owner’s existing support check is retained; its full-rank rows no longer read unused coefficients from an unwritten buffer. Conditional nearest-neighbor all-invalid errors identify inadequate support or numerical accuracy as possible causes, rather than attributing every failure to ties.
Conditional local-polynomial bandwidth searches again use the
existing original-coordinate rank/ridge admission when local QR rank is
ambiguous. Rejected numerical evaluations receive the existing
invalid-trial penalty, allowing search to continue; the selected
endpoint must still be raw-valid. This restores ordinary conditional
density and distribution NOMAD searches, including mixed-predictor
wage1 examples.
Conditional NN local-polynomial searches can select different local optima after changing predictor units even when fixed-candidate objective values agree. Existing multistart controls can help explore search sensitivity; rescaling does not guarantee the same selected bandwidth or fitted values.
Nearly coincident or exactly collinear predictor values (including tied integer-valued predictors), and minimally supported local designs far from the coordinate origin, can still trigger original-coordinate ridging after structural support admission and make conditional NN scores depend on predictor units. These remaining rank limitations are deferred; the structural-support correction does not change ridge coordinates or the frozen coefficient-accuracy threshold.
Conditional numerical failure state is cleared between trials
after owned work completes. In npRmpi, existing row
reductions retain shared failure metadata until that completion,
preserving rank-symmetric cleanup.
Beta generalized-neighbor local-constant derivative hats now retain observation identity for implicit training queries, including repeated values and chunked evaluation. Matrix/apply derivatives agree with the fitted gradients and their HC0 normalization. Explicit external queries retain their existing radius contract.
Conditional likelihood cross-validation reports a guarded evaluation on the coordinator whenever any computing rank used the existing nonpositive-fit rule. The counter still counts evaluations, not observations or MPI ranks; likelihood values and local-mode arithmetic are unchanged.
Bandwidth progress retains iterations and the actual multistart counter while search work continues after the requested starts. Completion and zero ETA are no longer displayed prematurely; failed searches clear the existing progress line without reporting success. Fit/evaluation heartbeat behavior is unchanged.
Nearest-neighbor bandwidth searches now identify an infeasible-bandwidth recovery attempt explicitly in the existing progress display, separately from refinement. Completed starts with remaining search work are labelled as continuing search.
Fixed-bandwidth conditional local-polynomial cross-validation now assesses rank after deleting the held-out observation, retains the existing ridge rule when required, and uses stable QR for unregularized deleted designs. Ordinary and all-large-bandwidth paths share this policy across polynomial degrees, avoiding cancellation and predictor-order sensitivity. This can cost more; fixed-bandwidth regression solvers and local-constant defaults are unchanged.
Conditional positive-kernel local-polynomial cross-validation now
assesses deleted designs in locally centered and scaled coordinates.
Uniform and second-order Epanechnikov generalized-neighbor objectives
retain accurate polynomial coefficients in their compressed integration
owner. Failed coefficient accuracy checks or exhausted integration
limits invalidate that trial through the existing penalty policy without
terminating the search. These corrections can substantially increase
computation time. Retained Linux benchmarks on representative univariate
cv.ls local-polynomial cases at sample sizes 200 and 400
measured about 16–28 times the earlier cost for uniform predictor
kernels and 4–5 times for second-order Epanechnikov kernels, with the
uniform-kernel ratio increasing with sample size in those cases. These
are case-specific cumulative costs of the numerical repairs, not
universal multipliers.
Conditional-density generalized-neighbor CVLS with beta predictors or bounded Gaussian predictors now uses the same whole-response criterion as unbounded predictors when the response domain is unbounded. This corrects the objective and can increase its cost; response boundary handling retains its own routes.
Single-index fits and hats no longer warn about a uniform-kernel order merely retained from their bandwidth object. Explicit fresh user requests for an ignored order still produce the advisory.
Beta-kernel generalized-neighbor regression now excludes a training observation from its own neighbor search while retaining its contribution to the fitted sum. Training fits, hats and AIC use that same identity; explicit external queries keep their separate neighborhood contract.
Conditional-density generalized-neighbor least-squares cross-validation now preserves local predictor differences in tightly clustered samples with uniform and second-order Epanechnikov predictor kernels. Higher-order signed Epanechnikov predictor kernels retain a known accuracy limitation on tightly clustered samples. The more stable calculation can cost more; default Gaussian predictor kernels are unaffected. Valid narrow response intervals are also retained instead of being rejected by the integration path.
Corrected unbounded conditional-density least-squares
cross-validation with generalized nearest-neighbor bandwidths to
integrate the squared leave-one-out fit using response bandwidths that
vary over the full response support. This covers Gaussian and
Epanechnikov kernels of orders 2, 4, 6, and 8, uniform kernels, and
local-polynomial and mixed-response models. Selected bandwidths can
change. These objectives can cost more than analytical
fixed/adaptive-neighbor objectives; see npcdensbw for the
distinction.
Generalized-neighbor density cross-validation preserves very narrow integration intervals that could previously be lost through rounding.
Single-index asymptotic fitting omits unrequested internal derivative standard errors while retaining derivative values, mean uncertainty, and default coefficient covariance. Requested public gradient errors are unchanged.
Bounded regression AIC objectives use self weights consistent with their fitted smoothing rows for fixed and nearest-neighbor bandwidths.
Bounded adaptive-neighbor scalar regression objectives use the same donor-centred normalization as regression fitting, with delete-one radii.
Bounded fixed-bandwidth regression CVLS objectives cancel their common continuous row normalization before kernel-pair reuse, agreeing with delete-one fits without per-pair continuous normalization corrections.
Conditional bandwidth scaling now uses response spreads when preparing response bandwidths, including conditional objectives and evaluation helpers.
Powered kernel weights respect the ranges initialized by sparse tree traversal, preventing reads of uninitialized weights while retaining trees.
Conditional-distribution hybrid bandwidth searches retain nearest-neighbor cache statistics from the completed Powell phase even when it does not improve the selected objective. Search results and cache computation are unchanged.
Klein-Spady single-index search and fitting now reject factor or non-binary responses with an early numeric-0/1 error. This prevents category codes from entering the binary likelihood or producing invalid fitted probabilities. Valid numeric responses, factor predictors and Ichimura behavior are unchanged.
Instrumental-variable regression refreshes its existing progress line during long kernel evaluations within bandwidth selection and fitting.
Instrumental-variable cv.aic bandwidth searches
retain the polynomial degree when retrying optimization, avoiding a
missing-degree error.
Smooth-coefficient bandwidth searches now refresh their progress heartbeat during long kernel-sum evaluations, without waiting for the next optimizer iteration or displaying internal kernel-row counts.
Conditional adaptive-NN likelihood and CDF objectives share the qualified fold-aware X-tree batches with density CVLS. External CDF grids now retain literal deleted-donor radii and duplicate-grid loss normalization. Existing tree selection, fixed/GNN owners and fast density CVLS overlap reuse remain.
NN linear/local-polynomial fitting retains full-sample prepared radii and uses bounded query-tree tiles for adaptive compact-kernel tree requests. Generalized-NN fits and batched apply align every response with tree-ordered donors. Exact full-support certificates retain varying-weight dense moments when no donor can be pruned. Fixed, tree-OFF and solver policies are unchanged.
Scalar adaptive-neighbor objective rows now reuse bounded tree-support masks for compact kernels. Explicit tree-OFF and noncompact-kernel rows retain their dense arithmetic; no statistical options are changed.
Local-constant conditional density/distribution fitting now honors explicit compact-kernel tree requests for generalized-neighbor bandwidths retained from CVLS objects. Admission is limited to second-order Epanechnikov/uniform kernels without boundary normalization. Search and automatic selection retain their independent policies; the additional LP fitting changes are listed above.
Explicit tree selection now reaches LL/local-polynomial generalized- and adaptive-neighbor CVLS/CVAIC search owners with second-order Epanechnikov and uniform kernels without boundary normalization. Adaptive searches reuse fold-aware bulk moments; generalized searches reuse sparse BLAS and an exact nonpruning certificate. Fixed-bandwidth search and regression automatic selection are unchanged; fitting and scalar adaptive changes are listed above.
Terminal nearest-neighbor bandwidth-candidate failures now suggest inspecting ties/counts or explicitly selecting fixed bandwidths. Bootstrap refusal and warm-start policies are unchanged; no retry or method substitution is added.
Ambiguous near-integer ordered support no longer silently changes the raw Li-Racine cumulative integration convention. Lattice-dependent refusals explain numeric precision and the explicit Racine-Li-Yan alternative. Defaults, normal weights, convolution and genuine fractional support are unchanged.
Regression conditioning diagnostics now distinguish numerical rank loss and explain raw-basis scaling and the explicit Bernstein option. Help pages clarify raw-polynomial instability; thresholds, defaults and computations are unchanged.
Internal bandwidth-metadata conversion in regression and conditional hat routes no longer reports an ignored uniform order as a fresh user request. This does not silence every downstream constructor or bootstrap-plot route. Explicit order requests still receive the advisory; kernel choices and stored orders are unchanged.
Terminal bandwidth-search failures no longer mislabel nearest-neighbor candidates as fixed bandwidths. Search and failure policies are unchanged.
Kernel documentation now states the unordered Li-Racine normalization and distinguishes raw ordered Li-Racine cumulative and convolution support.
Failed nearest-neighbor regression refits and leave-one-out hat calls now explain when the count exceeds the effective training-sample limit. Admissibility and successful computations are unchanged.
Quantile fits and their plot helper now retain separate training
and evaluation omission indices; rows.omit describes the
fitted output domain. Numerical values and NA restoration are
unchanged.
Hat apply/constraint methods and cached regression-hat prediction
now use the fitted factor-response coding for explicit factor payloads,
as for fitting. Single-index fitting also retains this coding for
alphanumeric responses. Regression fitting rejects reuse of a
numeric-response bandwidth object with a factor response when fitted
factor coding is absent. An explicit factor payload to
npreghat() retains its historical integer-code convention;
this is not fitted factor metadata. Native regression also checks
response-buffer extents before reading them; factor-fitted workflows
remain supported.
Direct regression consumers, including significance tests and bootstrap plots, now fit the retained physical bandwidth when bandwidth scaling is requested.
Kernel-weight and local-operator helpers now pass complete native options, avoiding an out-of-range option read in hat and bootstrap calculations.
Conditional-distribution generalized-neighbor bandwidth searches now honor explicit tree requests for second-order Epanechnikov and uniform kernels without boundary normalization. Automatic selection and other search kernel/bandwidth routes retain their existing policies and owners.
Conditional-distribution prepared bandwidth searches now include all response and predictor bandwidths and searched degrees in their objective-cache keys, preventing stale objectives when a candidate changes.
Adaptive-neighbor conditional-density least-squares cross-validation reuses analytical response overlaps in bounded blocks for one unbounded continuous response, including signed local-polynomial influence weights. Explicit tree requests use compact Epanechnikov/uniform predictor evaluation trees in original donor order. Fixed bandwidths and likelihood objectives are unchanged.
Univariate continuous generalized-neighbor density least-squares cross-validation contracts the response mean before whole-support integration. Gaussian integration uses reciprocal-coordinate quadrature; second-order Epanechnikov integration is polynomial-exact between support crossings. Workspace is linear in sample size, without a resident donor-pair matrix.
Mixed continuous/categorical generalized-neighbor density least-squares cross-validation now integrates the query-radius density over its full support and uses deleted-sample radii in the cross term. Correct integration can take longer than the previous convolution approximation.
Density and conditional density/distribution MADS bandwidth-object searches now enter the existing bandwidth-progress lifecycle, matching their default interfaces without adding nested progress owners or changing search results.
Conditional-density fixed-degree NOMAD bandwidth searches synchronize native preparation across the worker pool for local-constant and fixed- bandwidth models, avoiding a stalled search at preparation.
Regression bandwidth selection and local-polynomial operators again support categorical responses, including ordered linear predictors in partially linear models. Predictor support still includes declared unused levels.
Generalized-neighbor conditional-distribution cross-validation on external grids now uses the deleted donor sample for both predictor and response radii, retaining the empirical-grid convention, row/block work ownership and existing tree-selection policy.
Failed automatic non-beta generalized-neighbor conditional-distribution searches now recover within the deleted-sample count domain on external grids. Explicit/extended starts, selected solvers, empirical grids and other kernel/bandwidth routes retain their existing policies.
External non-beta generalized-neighbor conditional-CDF objectives reuse their prepared primary response radii instead of computing them twice; other response-context consumers and row/block selection are unchanged.
Generic nearest-neighbor leave-one-out hats now forward the canonical deleted-sample geometry to their existing kernel-weight owner. Ordinary kernel sums, native hat selection and rank-local ownership are unchanged.
Automatic nearest-neighbor density, regression and conditional-density recovery now uses each criterion’s deleted-sample count domain while retaining the full-sample CVAIC endpoint. Explicit and genuinely extended starts, selected solvers and healthy search paths are unchanged.
Generalized-neighbor deleted objectives and native leave-one-out hats now decode boundary counts on the deleted donor sample, retaining ordinary-fit geometry and existing computational owners. Invalid beta density CVML geometry fails closed rather than returning an objective of zero.
Automatic nearest-neighbor smooth-coefficient bandwidth searches can recover a valid deleted-sample boundary when tied conditioning values invalidate the initial search points. Explicit starting bandwidths retain their behavior.
Beta-kernel local-polynomial derivative hats preserve supported external rows when another query has all-zero kernel weights, returning undefined rows as NA with the existing warning. Complete-operator requests remain strict.
Local-constant conditional-distribution cross-validation uses absolute adaptive-neighbor predictor weights consistently in its legacy sums and kernel-weight exports. Fixed and generalized-neighbor weight units are unchanged. Separate nearest-neighbor fold-geometry limitations remain.
Private raw delete-one nearest-neighbor kernel geometry no longer consults ambient bandwidth-scaling state. Cold beta folded-weight calls use the existing context-aware distance owner directly; ordinary public kernel-sum conventions and existing computational ownership are unchanged.
Ordered Li-Racine weights and Racine-Li-Yan kernels accept finite fractional numeric distances in their original units. Declared unused levels remain part of RLY normalization; alphanumeric levels retain declared ranks. Wang-van Ryzin and normalized Li-Racine retain their unit-lattice contract. Singular elementwise bandwidth scores at zero smoothing report a precise condition. RLY vector evaluation no longer allocates an unused power table.
Native bandwidth preparation and local-polynomial operators retain declared categorical training support, including unused levels. Density, distribution, conditional and regression/LSQ routes now use the same support metadata as public fits.
Conditional-quantile inversion accuracy scales with the observed response span rather than its absolute location, so translating the response no longer loosens the requested accuracy.
Mixed-data beta copulas retain the applicable kernel components in each marginal, including categorical-only marginals, without discarding the beta kernels used by continuous variables.
MPI dispatch retains the selected package function when invoked through a local alias, computed function head or forwarding wrapper, including when a worker has a same-name global function. Argument expressions retain their original single-evaluation ownership.
Partially linear hat application preserves response column names after MPI row fan-out, matching local application. Single-response vector output and numerical values are unchanged.
Density bandwidth documentation now explains how to identify and interpret solutions at the fixed-bandwidth search floor, including beta CVLS sensitivity to endpoint observations.
Non-beta first local-polynomial hat derivatives now retain undefined external rows as NA with the existing row warning, consistently across matrix, application and prediction. Internal finite-operator consumers remain strict; native beta partial-row behavior is unchanged.
Automatic nearest-neighbor distribution-bandwidth recovery now respects the deleted-sample count domain. It can recover a valid ordinary candidate from a failed boundary start without replacing explicit or extended starts.
Conditional-mode omission metadata now describes the active evaluation result; separate training omission fields retain the training history. Estimates, standard errors, class probabilities and NA padding are unchanged.
Kernel-sum documentation distinguishes zero-diagonal leave-one-out sums from deleted-sample nearest-neighbor geometry, and raw weight exports from bandwidth-normalized sums. Estimator documentation now explains the existing generalized-neighbor distinction between training and explicit evaluation rows.
Higher and mixed local-polynomial hat derivatives with automatic ridging now share the canonical LP solve-admission policy. This avoids spurious ridge after translating predictors. Explicit positive ridge remains unchanged; default operators report all-zero external rows as unavailable.
Local polynomial uncertainty recognizes exact interpolation when an accepted unregularized local design has exactly as many supported observations as coefficients. Unidentified uncertainty is reported as unavailable instead of amplifying solve roundoff; point estimates and ridge admission are unchanged.
Conditional quantile plots preserve fitted numerical extraction controls, including resampled fits and bootstrap centers. Explicit plot controls take precedence; default and legacy-object behavior is unchanged.
Smooth-coefficient level hat operators include the fitted model’s intercept correction after a positive ridge, including categorical-profile and local-polynomial paths. Zero-ridge solves and ridge selection are unchanged.
Partially linear hat operators apply the fitter’s identification check to the residualized linear design, including subtraction error. Unidentified models now fail consistently instead of returning row-order-dependent hats.
Location-scale quantile fits retain trained formula transformations for prediction and scalar children of vector-tau fits. Native exdat is already in model coordinates and takes precedence without a second transformation.
Conditional quantile fits retain their resolved numerical extraction controls. Prediction reuses them unless explicitly overridden; old saved objects without these controls retain the historical defaults.
Single-index fitting, prediction and inference carry retained fixed kernel endpoints into their regression and kernel-sum consumers, including bootstrap resamples. The fitted domain no longer disappears during these calls.
The Gaussian2 boundary-density helper preserves its selected second-order kernel at very large bandwidths instead of switching to a uniform density. This removes an artificial discontinuity in fitted values and CV objectives.
Copula plots align surfaces and uncertainty layers with their probability axes when supplied grids are unsorted. Stored fits and returned plot data retain their original row order.
Copula probability-grid evaluation keeps marginal inverse grids inside the fitted kernel domain, allowing bounded kernels to retain their valid support when the requested grid extension reaches beyond it.
Mixed-data copula marginals now retain the bounds of their original variable, independently of where categorical columns occur in the data.
Smooth-coefficient evaluation preserves positional native predictor columns when preparing large-bandwidth eligibility geometry; harmless vector/frame naming differences no longer stop evaluation or bypass qualified shortcuts.
Regression hat applications use the regression’s retained numerical coding when borrowing an implicit factor response from a formula, bandwidth object or fitted model. Explicit numerical response payloads are unchanged.
Quantile prediction preserves native exdat coordinates for formula fits, avoiding repeated predictor transformations. Native evaluation retains precedence over raw formula newdata.
Regression hat applications now pass resolved degree, basis and Bernstein overrides consistently to every numerical owner, including single-response shortcuts and retained-operator prediction.
Prediction from a formula regression hat operator now evaluates newdata with the trained predictor transformations. Explicit native exdat takes precedence and is not transformed again.
Recomputing a retained regression hat operator now preserves whether its targets are training observations or external queries. Explicit new grids remain external even when their coordinates equal the training data.
npreghat() now dispatches fitted regression objects to their registered method before inspecting saved calls, preserving retained training inputs and ordinary subclass dispatch.
Quantile formula refits now consume the resolved missing-data policy before filtering numerical controls, allowing the documented replacement-data na.action override without weakening native argument validation.
Explicit gradient orders are now checked consistently by regression fits, regression plots and stored gradient/gradient-SE extractors. Unsupported higher orders no longer silently return first derivatives; existing LP partial availability and categorical first differences are preserved.
Conditional-mode class support now remains owned by the fitted training data. Evaluation response levels contribute to diagnostic tables without adding probability columns or changing fitted class probabilities.
Mixed convolution/tree kernel-weight exports now clear scratch entries outside the certified product-kernel support. Sums were already restricted to that support; raw matrices now agree with their kernel products.
Compact generalized nearest-neighbor convolution tree queries now account for both kernels’ bandwidths, retaining donors whose supports overlap. Fixed-bandwidth and adaptive-neighbor computational choices are unchanged.
Categorical kernel-sum scores now differentiate the requested convolution or ordered CDF operator, rather than substituting the ordinary-kernel derivative. Li–Racine convolutions also use their retained-support owner when scores or categorical profile caching are active.
Ordered factors with decimal-offset integer-spaced labels now preserve their intended distances in kernels, bandwidth scores, CDFs and overlap sums. Validation tolerates bounded floating-point representation error while continuing to reject genuinely fractional spacing; numeric gaps are retained.
Conditional-moment test covariance factors now use physical bandwidths, including for scaled objects. Quantile-test density weights use normalized kernel sums, preserving row/donor-local nearest-neighbor radii rather than treating neighbor counts as kernel volumes. This can change non-density- weighted NN tests; fixed-bandwidth standardized tests retain their existing cancellation of the common scale factor, up to roundoff.
Copula marginal estimators now use the physical bandwidth retained by the joint bandwidth object instead of interpreting scale factors as bandwidths. This aligns probability coordinates, density ratios, uncertainty and bootstrap marginal operators for scaled and equivalent unscaled objects.
Unconditional density and distribution fits now consistently use
the physical bandwidth retained by scaled bandwidth objects. This
corrects fixed beta-kernel estimates and dependent copula results when
bwscaling = TRUE.
Type-II significance-test reselection now replaces the physical bandwidth and matching scale metadata for the tested predictors together. Previously, scaled objects could retain the original smoothing parameters after a search.
Regression fitting now consumes the physical bandwidth retained
by its bandwidth object, as numerical hat operators already do. This
removes a legacy scale reconstruction that could fit a different model
when bwscaling = TRUE; unscaled fitting and bandwidth
searches are unchanged.
Generalized-nearest-neighbor distribution CV now evaluates each bandwidth neighborhood on the deleted sample. Empirical query rows retain their training identity; explicit grids retain external-query geometry. Canonical kernel factors are reused across folds, preserving tree and MPI ownership in the corresponding package. Corrected criteria can change selected generalized-NN bandwidths.
Automatic fixed-bandwidth beta density CVLS now requires a
positive scale.factor.search.lower when the complete
response sample contains repeated observations at a declared endpoint.
This conservative search restriction leaves the default 0.1 floor,
manual evaluation, and nearest-neighbor searches unchanged; it does not
insert a floor or jitter.
Beta density CVLS quadrature uses the interior-limit contribution of boundary observations instead of assigning positive integration weight to isolated endpoint spikes. This applies to unconditional and conditional objectives, including distributed quadrature. Ordinary PDF values and CV cross terms are unchanged; zero-concentration uniform components remain included.
Beta generalized-NN unconditional density and distribution fits now preserve training-query identity when choosing radii, as other continuous kernels do. Explicit external queries keep their external geometry; full-fit standard errors use the same corrected contributions. This can change training fits.
Bandwidth selection now reports activity during baseline-reference evaluations without advancing optimizer iteration counts. Native progress callbacks preserve interrupts instead of consuming Ctrl-C.
Adaptive nearest-neighbor distribution CV now evaluates donor radii on each deleted sample for beta kernels and external grids as well as empirical grids. Canonical CDF factors and occurrence-safe exclusion intervals are reused across folds. This can change selected adaptive-NN bandwidths; fixed and generalized-NN criteria are unchanged by this repair.
Ordered Racine–Li–Yan kernels now share one retained-support implementation for mass, CDF, bandwidth scores, overlaps and categorical profile plots. Regression CV paired moments preserve directed donor weights and row-specific AIC diagonals. Near-upper shortcuts certify the full retained support. Numeric ordered levels require finite increasing integer spacings; use nonnumeric ordinal labels when rank spacing is intended.
Explicitly disabling automatic dispatch now establishes local numerical ownership at estimator and bandwidth entry, including preparation and kernel sums. This prevents master-only calls from entering an unmatched native collective. Manual broadcasts and bootstrap worker dispatch retain their existing collective ownership; ordinary errors restore the pool state.
Automatic MPI dispatch retains already evaluated bandwidth arguments and shares one realization between formula detection and publication. Stateful native bandwidth expressions are no longer evaluated repeatedly; errors and interrupts propagate without retrying the formula probe.
Automatic density-equality test bandwidths now use one pooled-sample search and the harmonic-mean sample size to rescale its bandwidth. The same physical bandwidth is held fixed in both samples and all bootstrap draws. Categorical rescaling is constrained to the kernel’s legal maximum, with a warning when reached. Supplied bandwidths and manual scale-factor interpretation are unchanged; this is a selection policy, not a claim of power optimality.
Adaptive nearest-neighbor leave-one-out kernel moments no longer risk a crash from uninitialized state, including smooth-coefficient bandwidth selection.
Convolution kernel sums now compute requested derivative and categorical replacement products instead of reading uninitialized permutation buffers. Raw exported kernel-weight matrices also honor their documented independence from sum normalization, including adaptive nearest-neighbor bandwidths.
Scaled bandwidth objects now supply their retained physical bandwidths to categorical profile fits, ridge scaling, hat operators and wild bootstrap helpers. Fixed bandwidths equal to one are no longer misclassified as nearest-neighbor geometry by the regression validator.
Conditional properness certification recognizes normalized unordered Li–Racine response kernels. Bootstrap surfaces use the same properness certificate as the point estimator instead of renormalizing an already proper estimator on its finite plotting grid. Degree-zero conditional local-polynomial bootstrap helpers share the local-constant operator.
Quantile and classification formula fits preserve the effective evaluation rows when native evaluation arguments override newdata, without applying training omissions to the new sample. Their default methods retain positional manual bandwidths. Joint native inputs with inconsistent row counts are rejected before recycling in quantile, classification, significance-test and smooth-coefficient bandwidth routes.
Scalar bounded conditional CVLS now honors explicit hybrid quadrature ratios; uniform components retain the exact declared support endpoints. Gaussian2 auxiliary boundary-density moments use stable arithmetic at large bandwidths. IV derivative initialization rejects contradictory supplied values at repeated coordinates before bandwidth preparation. Bootstrap plots distinguish a legitimate NA-labelled factor level from a missing observation.
Uniform-kernel convolution no longer divides by its bandwidths twice. This corrects overlap sums and density CVLS objectives at non-unit bandwidths.
Gaussian eighth-order and Epanechnikov convolution calculations now use location-independent polynomial arithmetic. This prevents translation-driven cancellation from corrupting kernel overlaps and density CVLS objectives. Kernel definitions, bandwidth geometry and computational routing are unchanged.
Kernel sums with multiple weight and response columns now return the documented weight-by-response layout for every kernel and operator. Smooth-coefficient backfitting and IV moment consumers use that same layout.
Kernel-sum bandwidth scores now differentiate the Wang–van Ryzin and normalized ordered Li–Racine kernels correctly at all category distances. Normal kernel sums and categorical contrast definitions are unchanged.
Uncompressed density CVLS now squares sample counts in floating-point arithmetic, avoiding integer overflow that could corrupt bandwidth selection for large samples. Kernel calculations and computational routing are unchanged.
Bounded conditional-density nearest-neighbour CVLS now evaluates both objective terms using the sample with the held-out occurrence removed. Response quadrature reuses the existing nodes and bounded work tiles while selecting fold-specific radii. This can change selected NN bandwidths; fixed bandwidths and unbounded analytic-convolution objectives are unchanged.
Nearest-neighbour leave-one-out regression beta hats and smooth-coefficient fits, hats, uncertainty and CV moments now construct raw weights after excluding the held-out occurrence. Adaptive-NN LC moment calculations also retain donor-specific bandwidth normalization. Ordinary public kernel-sum zero-diagonal semantics and fixed-bandwidth geometry are unchanged.
Kernel sums with multiple weight columns and an omitted response now retain the correct output dimensions, including permutation sums. An omitted response acts as a column of ones; kernel calculations are unchanged.
Beta nearest-neighbour regression CVLS and density CVML use neighbourhoods with the held-out occurrence excluded. Unconditional density CVLS applies the same correction to its cross term while retaining the full-sample integrated squared density. Fixed-bandwidth calculations are unchanged.
Conditional properization no longer treats LC/degree-zero fitting alone as a guarantee of a proper density or CDF. The shortcut also checks kernel positivity, response normalization and nearest-neighbour orientation.
Fixed categorical class-probability effect plots now apply the fitted probability projection at both category endpoints before subtraction, matching public class-probability predictions.
Conditional fitted objects retain their resolved properization controls for prediction and plotting. Explicit consumer controls take precedence; older objects without retained controls keep the historical default behavior.
Conditional density/distribution properization groups conditioning rows by exact identity, avoiding merged slices after large coordinate translations or numeric factor-label rounding.
Categorical recoding and declared-support reconstruction preserve valid factor levels labelled NA without converting them to missing observations. Actual missing factor codes remain missing.
Formula fits with native evaluation data no longer restore training exclusions into an unrelated evaluation grid. Native evaluation arguments consistently take precedence over formula newdata in these routes.
Smooth-coefficient and partially linear fits honor separately supplied evaluation Z data when evaluation X defaults to the training data, and validate the paired evaluation row counts.
Explicit evaluation responses at training locations follow the training-row omission mask and native tree ordering. Single-index fits no longer overwrite those supplied responses when calculating goodness-of-fit statistics.
Retained-bandwidth conditional, single-index, partially linear and smooth-coefficient fits reject mismatched paired data rows before recycling can alter the sample.
Uniform native quadrature grids retain the exact supplied support endpoints, preventing roundoff from dropping a bounded-kernel endpoint contribution in conditional and unconditional density cross-validation.
Mixed categorical/beta density and distribution operators now pass the categorical-compression option to their shared native kernel-sum owner.
Mixed local-polynomial derivatives correctly differentiate every coordinate, including constant factors and degree-zero axes. All-zero derivative orders return the value basis, including its intercept.
Conditional fits, operators and bootstrap preparation consistently resolve retained scale factors to physical bandwidths, including the separate X/Y calculations used by local-polynomial fits. Resampling does not rescale the selected smoothing parameters. Bias-corrected bootstrap pilots preserve physical/scale-factor metadata in regression, conditional, smooth-coefficient and partially linear bandwidth objects.
Fourth- and sixth-order Gaussian convolution calculations now preserve translation and bandwidth-unit invariance. This corrects affected kernel sums and density cross-validation calculations.
Auxiliary beta density kernels consistently use dimensionless bandwidths on normalized support. Beta2 rejects h > 1/4 and inadmissible explicit grids; automatic starts and optimizer bounds respect that same domain.
Sampled cumulative quadrature is anchored at zero and invariant to repeated evaluation coordinates. Endpoint correction applies to uniform-grid totals only; irregular-grid totals use the nonuniform trapezoidal rule. Training multiplicities are retained, and the IV derivative iteration reuses prepared shared integration geometry. Entropy’s corrected-total arithmetic is unchanged.
Auxiliary boundary and shape-constrained distribution estimates integrate the retained fitted density from the true support lower bound, not the first requested evaluation point. Infinite tails are not truncated and shape QPs are not refitted for integration. Boundary proper=TRUE uses positive-part whole-support normalization instead of a constant shift that could create infinite mass. Sparse-grid CDF queries can have additional integration cost; CDF integration adds no cross-validation work or raw-density refitting.
The auxiliary Gaussian2 boundary kernel evaluates its removable moment-ratio limit at and near the support midpoint, including narrow-kernel ordinate underflow, instead of returning NaN. The mathematical kernel is unchanged.
Conditional-mean and quantile specification bootstrap refits retain model observation weights and, for mean models, offsets. Weighted rq models use their retained raw model frame rather than weight-scaled x/y components. Quantile tests reject a tau inconsistent with the fitted model, and both formula tests retain the correct omitted-row indices.
Auxiliary univariate density/CDF standard errors use the training sample size rather than the number of evaluation points. Shape-constrained density fits accept one-sided density bounds correctly; integral.equal uses the rightmost coordinate regardless of input order and rescales raw derivatives consistently with the density (log derivatives are unchanged).
Density-equality tests freeze one common declared categorical support, including unused levels and supplied bandwidth metadata, in every observed and bootstrap contraction. Raw numeric bandwidths no longer acquire different category normalizations in the two samples. Active ordered Racine-Li-Yan kernels are rejected in this test only. Cross-sample normalization no longer overflows when the product of integer sample sizes exceeds 2^31-1.
Conditional-mean and quantile specification tests accept manual bandwidths without duplicate kernel arguments, retain selected kernel metadata in all contractions, and keep na.exclude models on their compact estimation sample. They validate actual model components rather than the spelling of the model call; incompatible test/model response samples are rejected explicitly.
Formula column extraction and retained-role replay support quoted predictor names without silently renaming the data. Pipe-formula splitting distinguishes actual operators from punctuation inside quoted names or transformations. Fitting, refitting, prediction, significance tests and plotting share the same symbolic-name interpretation; formulas are not re-evaluated to decode names.
MPI apply and bootstrap collectors now limit the terminal
handshake after all numerical results are complete to 30 seconds per
outstanding handshake stage, and each cooperative cleanup attempt to 30
seconds in total, by default. Queued receipts are consumed before expiry
checks; acknowledging one worker does not extend another worker’s wait.
Separate npRmpi.fanout.terminal.timeout and
npRmpi.fanout.cleanup.timeout options control these maxima;
the latter replaces the session receive option for this purpose. Running
numerical work has no new deadline. Incomplete cleanup quarantines the
pool without returning partial results or automatically aborting
MPI.
Density-equality tests now use one common bandwidth in both samples and all bootstrap replications. Automatic selection uses pooled LSCV with the harmonic-mean reference-size adjustment described above; explicit CV-method choices remain available. One supplied bandwidth is reused without a search; incompatible two-bandwidth inputs are rejected. This test now explicitly rejects nearest-neighbour bandwidths and boundary-normalized/beta kernels, whose directed cross sums need not be symmetric as its statistic and studentizer require. Fixed unnormalized Gaussian, Epanechnikov and uniform kernels remain supported. No other estimator’s kernel or nearest-neighbour capabilities change.
Significance-test preparation uses local regression when automatic dispatch is disabled outside a collective broadcast, avoiding an unmatched MPI collective. Ordinary automatic and explicit collective preparation retain their MPI work sharing.
Bootstrap upper-tail p-values count ties as at least as extreme in all inference routines, consistently with significance tests. They remain ordinary bootstrap proportions (no add-one correction); zero p-values remain possible. Discrete/categorical tied cases may now report larger p-values.
Smooth-coefficient hat application with categorical profile compression now expresses a positive ridge in the same kernel-weight units as the matrix output. Bandwidth selection and zero-ridge arithmetic are unchanged.
Native data-first fitting calls such as npudens(x)
again pass observations to bandwidth selection instead of interpreting
them as bandwidths. Explicit manual-bandwidth and named training-data
calls retain their interpretation. MPI construction and fitting continue
through their collective owners.
Univariate entropy equality and symmetry tests retain declared categorical support, including unused levels, throughout probability summation and bootstrap resampling. Equality tests use common category metadata; ambiguous qualitative ordered supports require an explicit common level declaration. Corrected categorical statistics, selected bandwidths and p-values may differ. A shared plug-in selector now evaluates each sample’s own probabilities, fixing equality tests that supply only the first bandwidth. Independent missing-value omission is completed before overlap checks, and obsolete omission attributes no longer break numeric entropy integration. Categorical symmetry now rejects an observed or bootstrap reflection outside its declared support instead of silently losing those observations.
Bootstrap progress retains the latest completed work between heartbeats. Significance tests report completed internal tiles without reducing their numerical task sizes. Collective test consumers report completed batches, while operator rows/blocks remain distinct from bootstrap replications. Workers do not write to the console; quiet mode avoids completion beacons.
Bivariate entropy observed statistics share summation rows and quadrature evaluations across active ranks. Each row retains the full training sample; bootstrap draws, bandwidths and integration grids are unchanged.
Serial-dependence tests reject missing time points rather than silently joining observations across gaps. Supply a contiguous complete segment explicitly when the original series contains missing values.
Kernel sums, hat operators and quantile evaluation now distinguish current missing rows from historical na.action attributes on already-clean data. Supplying na.omit/na.exclude output no longer deletes unrelated rows a second time. Formula-result omission restoration remains unchanged.
Density-equality tests retain an individually supplied bandwidth while selecting only the missing counterpart. Incomplete rows are omitted once per independent sample before bandwidth selection and bootstrap resampling, avoiding inconsistent kernel-sum/count dimensions; at least two complete observations are required in each sample.
Significance tests keep their progress heartbeat active during long native bootstrap tiles and reuse an already-computed unrestricted fit when preparing streamed residuals. Bootstrap draws and test definitions are unchanged. Type-I preparation now uses the existing distributed regression computation, reducing large-sample preparation time at the cost of additional coordination for short jobs. Independently assigned bootstrap fits remain rank-local.
Density-equality tests display activity while computing the observed statistic after the bootstrap replications finish. Eligible observed kernel sums now share work across ranks instead of repeating the calculation on every rank.
Reusing a single-index bandwidth object created by a one-call formula fit now preserves its training omission map. With na.exclude, fitted values, standard errors, gradients and residuals retain their original row positions, including residuals requested after fitting.
Smooth-coefficient prediction and location-scale quantile SE extraction now preserve a computed NA standard error for a single unsupported evaluation row, as they already did for larger grids. Unrequested uncertainty still gives a no-search refit message.
Location-scale quantile prediction and retained-bandwidth refits now restore omitted rows consistently, including standard errors and multi-quantile residuals. Explicit replacement samples keep their own row positions. Scalar numeric I(…) terms also work in location-scale quantile evaluation and kernel-sum formula evaluation, as they do when fitting.
Formula regression, single-index and smooth-coefficient residuals now retain training-row ownership when an external evaluation grid has different missing rows. Smooth-coefficient coefficients and effects are restored alongside fitted values under na.exclude. External smooth-coefficient residual requests no longer depend on requesting standard errors.
Smooth-coefficient prediction and lazy residual extraction retain the fit’s iteration, tolerance, iteration limit and leave-one-out controls. Explicit prediction controls still take precedence.
Scalar numeric I(…) formula terms now use the ordinary numeric predictor type, without admitting matrix-valued terms or changing the stored formula.
Smooth-coefficient wild-bootstrap coefficient intervals now reuse the same categorical kernel normalization as their fitted moment equations. Density profile bootstrap intervals also retain the estimator’s normalized Li–Racine kernels for both unordered and ordered factors. Bootstrap draws, ordinary fitted coefficients and bandwidth selection are unchanged.
Local-polynomial fitting, operator and bootstrap rank certificates now count exact repeated design rows once, rather than treating duplicate observations as independent directions. Sparse supported designs use the existing ridge policy consistently across fits, hats, inference and counted bootstrap draws. Identity preparation is shared within each fitting invocation; bandwidth search and numerical ridge thresholds are unchanged.
Significance-test progress now shows the active predictor’s position and completed bootstrap replications, with call-wide elapsed time and ETA. Analytic non-rejection is labelled as skipped bootstrap work.
Partial response replacement with formula-derived bandwidth
objects now uses retained-sample row order, before jointly applying the
effective NA policy. Refits retain the replacement sample without
changing the original bandwidth object. Original-data replacement
belongs in data; it cannot be combined with a partial
response. Bandwidth selection is not repeated.
Native-object standard newdata now matches named
columns to the retained variable names in fitting and prediction.
Reordered or extra columns are handled consistently; missing, duplicate
or ambiguous names are rejected. Explicit native evaluation arguments
remain positional and take precedence. Unnamed single-role matrix/vector
inputs remain positional. Copula newdata uses the
documented probability-coordinate names.
npreghat rejects explicit nonzero ridge
requests on computational owners that cannot honor them. Default
automatic numerical ridging and supported generic mixed-derivative ridge
requests are unchanged.
Density/distribution hat and bootstrap adapters now preserve normalized Li–Racine ordered kernels, including conditional response kernels. This corrects affected ordered-factor operators, bootstrap intervals and conditional local-polynomial contrast/higher-derivative uncertainty. Raw regression and kernel-sum ordered-kernel conventions are unchanged.
Hat derivative selectors now validate orders before integer conversion and honor named coordinates and explicit prediction overrides. Index hats accept matrix responses consistently and retain singleton matrix dimensions. Supported explicit hat ridges are retained when rebuilding an operator.
Fits reusing retained native bandwidth data now give explicit replacement responses precedence over retained defaults. Copula predictions preserve inversion settings; conditional-mode predictions preserve the requested probability projection. Copula joint and marginal calculations use a common named column order and the actual replacement sample size.
Formula kernel-sum weights and location-scale regression scales now follow the response/predictor subset, time alignment and missing-value selection. Full-length auxiliary inputs refer to the original sample; unambiguously preselected shorter inputs remain supported.
Native one-call fits pass their already evaluated training data to the bandwidth constructor. Side-effecting or random data expressions are no longer evaluated again for that handoff; fitting and retained bandwidth data use the same realization, including MPI dispatch.
Local-polynomial fitting, hats and related inference now consistently apply the existing ridge rule when fewer nonzero-weight donor rows contribute than there are basis coefficients. This corrects unstable fit/hat disagreement at structurally underdetermined rows, including mean-only fits. Bootstrap rank counts use independent donor rows rather than resampling multiplicities. Bandwidth-search objectives and the ridge magnitude and numerical thresholds are unchanged.
The retained-sample storage documentation now notes that MPI materialized stored calls can contain another copy of training values. No storage or argument-ownership policy changes in this follow-up.
Conditional gradient bootstrap preparation reuses the canonical internal bandwidth builder and finalizer instead of repeating public constructor setup. Each resample still uses its own geometry and the same numerical fit.
Bootstrap plot summaries now align categorical draw columns with their actual evaluation labels, including declared but unobserved factor levels. This fixes unused-level plots across regression, conditional and unconditional, partially linear and smooth-coefficient families without dropping levels or changing kernels, draws or intervals.
Native bandwidth-object fits now honor newdata using
the corresponding prediction interface’s evaluation roles instead of
silently returning the training fit. Conditional and semiparametric
frames must identify their roles by retained column names; explicit
native evaluation arguments keep precedence.
Exact wild regression bootstrap helpers restrict LC derivative-operator reuse to its intended continuous LC/LP0 first-derivative requests, and align missing evaluation rows before blocking. Other derivative requests retain their per-response estimator, including the existing beta-kernel path.
Ordered kernel-sum contrasts validate evaluation category indices before native work and initialize the cached lookup on the first row. Invalid contrast categories now give a clear error instead of an invalid memory read; ordinary ordered-kernel extrapolation is unchanged.
Adaptive nearest-neighbor leave-one-out regression hats now supply matching single-evaluation-point tree geometry to compact-kernel traversal, avoiding an out-of-bounds read without disabling tree evaluation or changing radii.
Interval cap widths now use horizontal display spacing consistently for level, gradient and partially-linear coefficient plots, independent of the response-axis range. Exact-zero intervals, interval endpoints and bootstrap draws are unchanged.
Smooth-coefficient wild-bootstrap coefficient-gradient plots now reuse a direct, blocked coefficient projection across bootstrap responses. The projection retains the fitter’s normalized moments, accepted ridge and intercept correction for LC/LL/LP and fixed/generalized/adaptive NN bandwidths. Pilots, draws, RNG order and intervals are preserved to numerical rounding. Mean plots and pairs/block resampling retain their existing owners.
Local-constant and degree-zero local-polynomial continuous-gradient wild bootstrap plots with Gaussian, Epanechnikov and uniform kernels now reuse the exact derivative operator across responses, including local-smoothing quantile plots. Operator rows are blocked for large grids. Bootstrap draws, pilots, intervals and RNG ordering are unchanged to numerical rounding; pairs/block resampling retains its own geometry. Beta kernels retain their per-response calculation because endpoint derivative cancellation can depend on the response. The canonical LC derivative operator now returns NA at external rows whose computed kernel weights are all zero, matching direct fitting; complete-row internal consumers still reject them. Invalid radii and other nonfinite results are not converted into partial results.
Plot interval ranges now align NA-padded categorical panels with
their actual interval rows. Unequal partially linear panel sizes no
longer generate a recycling warning with band="all";
interval values and axis ranges are unchanged. Inconsistent finite panel
data are rejected rather than recycled.
Native autodispatch now consumes the actual method’s already-owned data promises. A side-effecting data expression is no longer evaluated again after generic dispatch has forced it. Deliberately rewritten leaf inputs and unrelated wrapper locals remain separate from those argument owners.
Formula training now resolves character NA policies through the
same owner as stats::model.frame, so an unrelated
caller-local na.omit no longer changes the sample. Explicit
NA function objects retain their own behavior. With replacement training
data, an explicitly supplied na.action takes precedence
over the saved policy, including NULL, and the fitted
object retains the effective policy. Native complete-case exclusions
remaining after a permissive policy are now recorded with the retained
sample rather than lost from formula omission bookkeeping.
Internal native bandwidth children without an original
construction call no longer acquire an executable placeholder call.
Their retained-sample refits, predictions and plots remain supported;
update() on a call-less child now fails instead of
potentially using unrelated caller data and selecting new
bandwidths.
Fits with replacement training data now retain that fitted sample for later refits, prediction, plotting and inference. Native bandwidth constructors for regression, density/distribution and semiparametric methods also retain their training values, so rebinding caller variables does not change later results. Formula and native sample ownership no longer compete in LSQ scale-pilot children. Objects saved without retained samples still need their original data or explicit replacement inputs.
Positional numeric bandwidths in one-call fitting are now honored like named bandwidths, without unintended bandwidth selection. Existing inline formula and wrapper syntax is unchanged.
Ordered categorical contrasts correctly initialize the category lookup when the first evaluation category is zero. This corrects affected local-constant gradients, standard errors and their downstream bootstrap/test consumers.
Interval-cap widths now use display geometry consistently across response scales, including singleton evaluation points.
Partially linear and local-smoothing quantile child bandwidths
retain their own native samples and responses for later use. This is
sample ownership, not a general environment-compaction guarantee: user
formula and custom function environments remain available and can retain
unrelated caller objects in saved files. Retained samples also increase
object storage; derived children may retain separate copies. Serialized
size can exceed the size reported by
object.size().
Native arguments forwarded positionally through wrappers now retain their actual promise owner. An unrelated same-named wrapper local can no longer silently replace the supplied training data.
Formula bandwidth objects now retain their prepared training sample and NA policy. Rebinding constructor variables (for example in a model-building loop) no longer silently changes later refits, predictions, plots or tests. Explicit training-data overrides remain available. Objects created by older versions without retained samples still require their original data bindings. Single-index bandwidth plotting uses the same saved-data owner.
Arguments forwarded through ... wrappers are honored
as in direct calls: an explicit bws is no longer ignored in
favor of bandwidth search. Saved formula objects also retain forwarded
or caller-local na.action settings when refitting,
predicting, plotting or running significance tests. Native-data wrappers
can forward positional data or the txdat/tydat
aliases without requiring caller-local argument names on
workers.
Ctrl-C during MPI fan-out remains an interrupt after worker cleanup, rather than becoming a “bad error message” error that ordinary error handlers could swallow and continue past.
Formula constructors supplied inline are evaluated once per invocation, including the one-call bandwidth/fit route. Existing formula values and literal formulas remain supported; callers need not construct a formula in a separate statement. Forwarded calls retain lazy subset evaluation.
Bootstrap fan-out and the MPI apply collectors drain outstanding
worker replies before rethrowing supported master-side errors,
interrupts or dispatch timeouts. Operation identities prevent stale
replies from being published by a later call. Cancellation now stops
workers at existing task or bootstrap-chunk boundaries; an
already-running task may finish first. A second interrupt during cleanup
returns control with the pool explicitly quarantined, without returning
partial results or dispatching new work. The existing positive session
receive timeout also bounds each cleanup attempt; its zero default
remains unchanged. Explicit npRmpi.quit(force=TRUE) can
resume an abandoned cooperative cleanup and close the pool once all
pending communication is retired. Finalizers do not silently resume
quarantined work, and no automatic abort is used. Lost ranks and failed
native MPI operations remain outside this recovery guarantee. An
existing visible progress owner reports cleanup without implying
completed inference.
Streamed non-studentized categorical significance-test bootstrap statistics now use the same generalized-nearest-neighbour training-row geometry as the observed statistic and public regression refits. This can change affected bootstrap statistics and p-values; pivot=TRUE is unchanged.
Conditional density/distribution formulas reject unsupported transformed responses instead of silently discarding their transformations. Prepare the transformed response in the data, or use the native data interface. Formula routes also reject unsupported RHS offset specials before evaluation; supported response/predictor transformations and ordinary variable names remain unchanged.
Local-constant wild-bootstrap gradient plots no longer draw unused task seeds after generating master-owned multipliers. Later panels now use the same draws across worker counts and chunk sizes as the serial package; historical seeded bands may change.
Automatic regression wild-bootstrap pilots now use fitted training-row geometry whether plotting starts from a fitted model or its bandwidth object. This corrects generalized-nearest-neighbour bands that previously depended on that entry route. Explicitly supplied pilots and external evaluation geometry are unchanged.
The significance-test documentation now notes that studentization can improve size behavior without uniformly improving finite-sample calibration or power. The default remains pivot=TRUE.
Formula fitting defers data-dependent subset expressions to the model-frame owner rather than forcing them during argument dispatch. One-call fits and constructor-first fits now use the same selected rows.
Partially linear prediction standard errors use the prediction’s actual retained training rows, including data overrides, and preserve na.exclude padding of evaluation rows. Public residual padding is unchanged.
Vector-quantile least-squares predictions prepare formula newdata once and share that same evaluation sample across quantiles.
Partially linear formulas now prepare their response, linear regressors and smoothing variables together once, retaining trained transforms and a common subset/time-aligned sample through fitting, prediction and plot preparation.
Regression hat/significance helpers and smooth-coefficient plots now resolve saved training-data expressions in their original call owner, including bandwidth objects constructed inside wrappers.
Copula formula construction and fitting now share the same prepared sample, including transformed, subsetted and time-aligned inputs. Automatic grid dimension validation still precedes bandwidth search.
Smooth-coefficient formula construction, fitting and evaluation reuse a single prepared sample per transaction and retain portable trained terms. Named MPI formula calls are prepared before worker dispatch.
Single-index formula construction and prediction now use the shared single-evaluation preparation route and portable trained prediction terms. Named formula calls no longer depend on argument order or send unevaluated formulas to MPI workers.
Quantile and conditional-mode one-call formula fits also reuse
their prepared training sample. Conditional-mode response validation
still occurs before bandwidth selection. Saved-object refits now reuse
the retained training sample; explicit evaluation data still use the
trained formula terms. Positional quantile formula calls with supplied
numeric bandwidths no longer start an unintended bandwidth search; an
explicit positional bandwidth.compute = TRUE request
retains its existing search behavior.
Conditional density/distribution formula fitting now reuses its prepared training sample, avoiding repeated evaluation of predictor expressions. Saved conditional formula terms retain portable prediction metadata for refitting, quantile/mode evaluation and plotting.
Local-polynomial calculations now retain the same native polynomial-term order as np. This removes avoidable serial/MPI discrepancies that can be amplified in poorly conditioned fits; the solver and ridge policy are unchanged. This is not a new stabilization method for ill-conditioned data.
Fixed/geometric plot-bootstrap draws now use a replicate-ordered random stream, independent of processing chunk size and MPI worker count. The block law is unchanged, but historical seeded bands may change. The stream follows the previous single-replicate ordering; IID/wild draws are unchanged.
Rank-local regression corrections retain their R and native execution context together, repairing adaptive-NN local-constant bootstrap-gradient plots; nested contexts restore the preceding option and communicator state.
Generalized-NN fitted-row categorical effects and their standard errors now retain training identity for local-linear and local-polynomial regression, as local-constant regression already does. Explicit evaluation effects keep external-query radii. Fitted means and continuous derivatives are unchanged.
Time-series formula alignment preserves the columns belonging to each matrix-valued expression, preventing a later formula variable from being replaced by a column of an earlier expression. Existing restrictions on matrix predictors are unchanged.
Regression and unconditional density/distribution formula fits
evaluate each training expression once per constructor/fit operation.
This avoids inconsistent samples from random or stateful expressions and
retains prediction-transform metadata. Later refits still evaluate their
inputs. Stored formula objects for these families can be used by either
package without loading the package that created them. Unconditional
refits, predictions and plots resolve function-local data through the
retained bandwidth call, including after MPI fitting. Automatic
regression calls using formula = follow the same
preparation and fitting path as positional formulas.
Kernel-sum formulas and named unconditional density/distribution formulas prepare their inputs before worker dispatch. Caller-local variables and transformations no longer cause worker lookup failures or leave the master waiting after such failures. Numerical work retains its existing MPI routes.
Qualified Apple Silicon builds accelerate compensated local-polynomial regression influence calculations used by requested standard errors and studentized tests. The calculation, defaults and bootstrap random-number sequence are unchanged; other builds retain the existing scalar path.
Copula standard errors and plots recover physical evaluation coordinates independently of sanitized output column names. Transformed, lagged and reserved predictor names no longer cause plotting failures or substitute training/probability coordinates for a requested grid. Public output columns and the underlying estimation and uncertainty formulas are unchanged.
One-call npreg() formulas again align lagged
time-series variables before bandwidth selection, matching
npregbw() followed by npreg(bws = ...). This
repairs a 0.70-1 regression that could silently fit unaligned lag
columns, or reject them as collinear, and affect subsequent inference
and plots. LC, LL and LP use the same restored formula-constructor
route.
Lagged time-series formula alignment is also applied to single-index, least-squares quantile, kernel-sum, copula and IV formula preparation. Quantile, conditional-mode and regression-hat evaluation reconstruct the aligned predictors from the supplied new data, including transformed terms, without requiring columns named after the expressions. Ordinary data and explicit native-array alignment conventions are unchanged.
Fixed-block and geometric-block bootstrap plot calculations now receive master-owned random-number states instead of drawing on unseeded worker streams. Repeating a seeded call with the same worker/chunk configuration is reproducible. The sampling law is unchanged; different worker counts or chunk partitions need not produce identical draws.
Conditional-distribution bootstrap plot kernel operators now follow the native continuous/unordered/ordered data layout. This corrects bands and bias-corrected centres for mixed-data models whose input-column order differs from that native layout.
Ordinary regression inference now normalizes training fitted residuals by the norms of their actual residual-smoother rows before forming level, derivative and paired categorical-contrast sandwich errors. This can also change continuous and categorical studentized test statistics and P-values, not just reported standard errors. It uses the pre-smoothing residual correction of Ruppert et al. (1997), not a separate variance-function smoothing estimator. Requested errors that genuinely lack donor residual information are reported as unavailable, separately from certified zero influences. No variance floor, row deletion or bootstrap-draw filtering is introduced. The correction does not remove smoothing bias or guarantee finite-sample calibration under arbitrary heteroskedasticity or model selection.
Requested categorical regression effects use stable paired-influence arithmetic with and without standard errors. This can change previously cancellation-dominated gradients and downstream test statistics. Mean-only fitting and bandwidth search are unchanged. Residual preparation is confined to requested inference, but explicit categorical-gradient requests also incur more careful arithmetic when SEs are off. The correctness changes can increase these requested computations. Single-index mean/gradient errors and transformed-response least-squares quantile errors inherit the shared correction; separate coefficient covariance formulas do not. Help distinguishes this residual correction from numerical ridging motivated by Seifert and Gasser (2000).
Studentized joint npsigtest() calls reuse each
bootstrap regression fit across tested categorical predictors within
each worker’s existing bounded response tile. Statistics, resamples and
random-number consumption are unchanged. Individual studentized
categorical bootstrap fits use the same worker-local native contrast and
response-specific sandwich calculations but omit unrequested gradient
and contrast-SE consumers. Default full regression output and
unstandardized tests retain their existing computational paths.
npsigtest() now defaults to
pivot = TRUE for continuous and categorical predictors,
including joint tests. Explicit FALSE retains unstandardized statistics;
NULL is no longer an input mode. Categorical studentization uses
response-specific paired-contrast sandwich errors, with additional
computation confined to requested inference. An entirely zero observed
effect gives analytic non-rejection without bootstrap fits; skipped
columns and executed counts are explicit. Unexplained undefined standard
errors produce a diagnostic, never a variance floor or
fallback.
The getting-started guide demonstrates prediction and no-search refitting for uncertainty and gradients, distinguishes computation from extraction, and summarizes migration from 0.70-5. Package help clarifies practical computational limits; build notes distinguish historical backend evidence from current installation guidance. No estimator behavior or defaults change.
Entropy tests refresh elapsed-time progress during native summation and quadrature using the shared transient owner. Original-statistic, lag and bootstrap phases clear on completion or failure; bootstrap counts advance after their statistics finish, without changing resamples or calculations.
Progress displays use consistent work counts, elapsed-time and ETA wording. Narrow consoles preserve complete counters before optional detail, and wide-character labels are shortened by their displayed width.
Requested adaptive-NN standard errors and statistical-test bandwidth phases use the existing transient progress owners. Supplied bandwidths no longer produce a misleading standalone bandwidth-computation notice.
Explicit fixed-bandwidth conditional bootstrap bias-corrected plot centers now use one mixed-data donor-mixture pilot and its matching PDF/CDF or derivative/contrast reference. Categorical conditioning pilot lambdas generate normalized transitions on the declared levels; paired contrasts share each resample. Level refits now retain the selected LC/LL/LP estimator instead of substituting an LC ratio for LL/LP. These corrections can change explicitly requested bias centers, including continuous-only panels. Ordinary fitted values, estimate-centered variability draws and search are unchanged. NN, proper-projection and quantile bias-center exclusions remain.
Smooth-bootstrap Epanechnikov perturbations now use the same standardized kernel scale as estimation. This corrects explicitly requested unconditional and conditional density/distribution bias centers; Gaussian/uniform draws and RNG consumption are unchanged. It does not change ordinary bootstrap variability draws.
Uniform nearest-neighbour kernels now apply their documented strict support boundary to the unstandardized distance. This corrects adaptive- and generalized-NN results that could depend on evaluation batching, tree partitioning or affine rescaling. Dense and tree consumers share the same membership decision, including normal factors in gradient and integral products. Fixed-bandwidth arithmetic, other kernels and tree eligibility are unchanged; affected uniform-NN fitted values and search objectives may change where reciprocal rounding previously admitted a boundary point.
Unconditional adaptive-nearest-neighbour standard errors use correctly aligned transient workspaces on platforms where extended precision requires stricter alignment. The formula, precision and SE-off computation are unchanged.
Local-linear and positive-degree local-polynomial conditional density, distribution and quantile fits now provide requested categorical-contrast standard errors. Paired endpoint influences retain their covariance; quantiles use each endpoint’s own quantile and density. This includes fixed, generalized-NN and adaptive-NN bandwidths conditional on the realized geometry. Existing point estimates, continuous errors and SE-off computation are unchanged. Nonsmooth or nonfinite influences remain explicitly unavailable.
Bootstrap plots using the shared estimator dispatcher now retain one display owner and elapsed clock across their scheduled targets, with an approximate overall ETA. Copula bootstrap interval preparation uses the same scope. Numerical work and standalone-helper behavior are unchanged; conditional-mode and LSQ-quantile displays and the frozen bootstrap shortcut retain their existing behavior.
Conditional-mode fitting with MPI autodispatch no longer fails
with object 'allow.external' not found. Internal
row-availability controls now retain their values across distributed
conditional fits and proper slices.
Conditional density/distribution bootstrap gradient plots now retain the requested physical predictor column. Categorical panels previously could receive a continuous derivative’s bootstrap draws, or fail when predictors were interleaved. Their intervals now target the displayed first difference. Explicit fixed-bandwidth bias centers now use the mixed-data pilot described above, including categorical targets.
Conditional bootstrap gradients reuse the existing evaluator with a private single-target demand, avoiding unrequested R-side categorical differences and higher-derivative restoration. Native computation, exact NN resampling, random draws, public fitting and asymptotic-error behavior are unchanged. The mixed-data smooth-bootstrap pilot also reuses this single-target evaluator; its separate probability-law corrections are described above.
Conditional-quantile bootstrap gradients likewise compute only the requested R-side categorical quantile contrast. The original selected-CDF inversion, density evaluation, native derivatives and requested endpoint inversions are retained; full-output fits, tau layouts and LSQ quantile owners are unchanged.
MPI conditional-gradient and quantile level/gradient bootstraps now share one original-data center across worker tasks, avoiding discarded repeated fits. Replicate counts, seeds, task ownership and collected results retain their existing contracts; no scheduler or native arithmetic is changed.
Conditional density/distribution fitting now consistently retains explicit external rows with all computed explanatory-kernel weights zero as NA, with one notice. The policy covers LC/LL/LP, requested inference and categorical counterfactuals without changing supported estimates or valid zero response contributions. Proper projection retains unsupported fixed-X slices while projecting supported slices; prediction and plot consumers preserve status.
Conditional quantile inversion now preserves explicit base-support status for external rows across scalar/vector tau, prediction, inference and MPI tau blocks. Unsupported outputs are NA with one notice; genuine numerical and required internal-evaluation failures remain errors. The point-only constant-response shortcut is unchanged and does not preflight X support. Conditional-mode evaluation likewise preserves one final notice without changing supported class/probability calculations.
Identified post-preparation local-row failures in the conditional native owner now agree across MPI ranks and complete owned cleanup before raising the original error. This does not cover pre-loop preparation failures, arbitrary native collective unwinds, or rank/transport loss.
Requested unconditional adaptive-NN density/distribution standard errors now include the same-sample influence of donor-specific radii, with a two-sided NN rank-spacing pilot. The joint influence retains cross-coordinate and categorical-kernel covariance. Gaussian/Epanechnikov density and distribution kernels, and uniform kernels, are supported on regular interior NN ranks without finite kernel bounds. Uniform density errors include the joint marked moving-support faces using sample-quantile/spacing pilots; strictly absent faces have zero motion. Unqualified boundary-pilot, bounded, extended/saturated-rank or degenerate-spacing cases retain point estimates and return NA standard errors with one availability notice. This changes previously incomplete ANN uncertainty, not point estimates, bandwidth search or SE-off work. Bandwidth selection uncertainty is not included.
Requested fixed-bandwidth copula density standard errors now include the same-sample covariance of the joint and all marginal density estimates. The centered ratio influence is reduced in bounded query tiles; MPI keeps marginal kernel weights on their owning rank and communicates only SEs. Point estimates, bandwidth search, SE-off work, distribution copulas and NN uncertainty are unchanged. This correction holds marginal evaluation coordinates fixed; it does not include quasi-inverse or bandwidth uncertainty.
Requested conditional density/distribution LP standard errors now follow the fitted regularized operator when explanatory-kernel weights are constant. This corrects level and native first-derivative uncertainty after ridging; fitted values and point gradients are unchanged.
Requested scalar conditional adaptive-NN standard errors now use the complete empirical ratio influence for levels and existing continuous first derivatives, including continuous/mixed responses and categorical-only predictors. This corrects their physical bandwidth scaling while holding the selected model, predictor geometry and realized response radii fixed; sampling or selection uncertainty in these quantities is not included. Point estimates and gradients are unchanged. Local-polynomial, beta-kernel and paired categorical-contrast errors retain their separate targets.
Fixed/generalized-NN conditional density and distribution standard errors with categorical variables now retain their joint categorical second moments. Conditional CDFs and densities without continuous responses also retain numerator–denominator covariance, so a constant response-kernel contribution has zero variance. Pure-category profile and ordinary paths agree. Point values, point gradients, likelihoods, SE-off work and existing beta/positive-degree LP/paired categorical-contrast owners are unchanged. Broader adaptive-NN variance limitations remain deferred.
Fixed/generalized-NN unconditional density and distribution standard errors with mixed data now retain squared joint categorical factors within their existing leading continuous-bandwidth approximation. Constant categorical factors consequently scale uncertainty consistently with the estimate. Point estimates, likelihoods, SE-off work, pure-continuous/pure-categorical conventions, beta kernels and adaptive-NN errors are unchanged.
Partial-linear fitting and plot/bootstrap helpers now consistently reject numerically unidentified residualized linear regressors across BLAS implementations. The shared check accounts for column scale and residual formation precision while retaining the existing QR, accepted coefficient solve and covariance calculation.
Public single-index fitting and prediction now return
NA with one notice for external rows whose computed kernel
weights are all zero, consistently across point-only, asymptotic and
bootstrap requests. A bootstrap draw that loses support makes the
affected uncertainty unavailable without replacing a supported original
point estimate. Required training ratios remain strict; supported
values, resampling and computational owners are unchanged.
Unconditional distribution fitting through a named formula now
preserves an explicit se = TRUE request, matching the
bandwidth-object route and serial np. Omitted or false requests still
avoid uncertainty computation.
Requested higher-order conditional density/distribution LP
derivative standard errors now use the existing local working variance
and the actual derivative weighting row, including its accepted ridge.
Fixed and adaptive-NN point owners are preserved; generalized-NN
higher-point restrictions remain. These errors condition on the selected
bandwidths, radii and local map. No supplementary uncertainty work is
requested with se = FALSE.
Missing stored gradients or standard errors now consistently explain how to refit with the retained bandwidth object without repeating bandwidth search. Unsupported uncertainty targets retain their separate diagnostic messages.
Model-specification testing now uses the shared bandwidth progress owner, clearing its transient line at completion instead of leaving a permanent “Computing bandwidths” message. Test calculations are unchanged.
Requested conditional local-polynomial standard errors in the
general fitting path now use the accepted fit’s intercept ridge
correction. Failed covariance blocks or invalid variance calculations
return NA only for affected uncertainty outputs, including
beta-response level errors. Point estimates, search,
se=FALSE work and derivative availability are
unchanged.
Requested smooth-coefficient standard errors now use the same
accepted moment system and intercept ridge correction as the returned
point fit. Supported signed kernels no longer receive a separate
covariance-only ridge. Fitted values, coefficient effects, search and
se=FALSE work are unchanged; unavailable covariance retains
its existing NA output policy.
Copula fitting accepts named se=FALSE/TRUE,
defaulting to FALSE. Requested asymptotic errors are stored at fitting
time; se() only reads stored results and explains how to
refit with the bandwidth object when errors are absent. Explicit
prediction/plot inference requests retain their existing uncertainty
target; point estimates and NN geometry are unchanged.
Unconditional density and distribution fitting now accept named
se=FALSE by default, skipping uncertainty-only computation
while preserving point estimates. Use se=TRUE to retain
asymptotic standard errors or predict(..., se.fit=TRUE) to
request prediction errors. Extracting absent errors gives guidance for
refitting with the existing bandwidth object, without repeating
bandwidth search. Asymptotic plot and copula consumers continue to
request the uncertainty they need.
Partially linear fitting now defaults to se=FALSE,
avoiding coefficient covariance work unless requested. Use
npplreg(..., se=TRUE) for coef(..., se=TRUE)
and vcov(); missing-output messages show how to reuse the
bandwidth object without repeating search. Explicit prediction standard
errors and asymptotic plots continue to request their needed
work.
Conditional density and distribution fitting now have a
named-only se=FALSE default. Disabled errors skip
uncertainty-only native work and are stored as NULL; fitted
values and requested gradients are unchanged. Use se=TRUE
for available asymptotic errors, or refit a retained bandwidth object to
obtain them without repeating search. Prediction with
se.fit=TRUE requests the needed work explicitly.
Quantile and conditional-mode fits now accept named-only
se=FALSE. Uncertainty-only evaluation and storage are
skipped by default, independently of the existing gradient controls.
Explicit prediction or asymptotic plot requests enable their required
errors; stored-result extractors give a bandwidth-reusing refit
instruction when uncertainty was omitted. Point quantiles, class
selection, probabilities and requested gradients are unchanged.
Ordinary scalar conditional categorical-gradient standard errors
now use complete paired same-observation influences, including endpoint
covariance. Requested errors use an additional streamed inference pass
and can increase fit time when categorical uncertainty is retained with
gradients=TRUE. Point estimates, gradients, bandwidth
selection and computational selectors are unchanged. The default
gradients=FALSE and internal point-only and bootstrap
consumers do not request this additional work.
Conditional local-polynomial fits now retain available first-derivative standard errors when other requested derivatives exceed their fitted degrees. Existing point gradients, higher-order availability and computational owners are unchanged; point-only internal consumers request no extra errors.
Conditional generalized-nearest-neighbor training fits with ordinary explanatory kernels and a beta response kernel now exclude the identified training occurrence when selecting explanatory radii, retaining it in the estimation sum. Affected fitted values, derivatives and standard errors use the corrected geometry; explicit evaluation queries and beta explanatory kernels retain their existing behavior.
Partially linear coefficient standard errors and covariance now allow conditional heteroskedasticity through a residual-score sandwich, retaining the existing finite-sample multiplier. Coefficients, fitted values, bandwidth selection and the separate fitted-value prediction-error calculation are unchanged. Coefficient intervals and dependent asymptotic plot terms use the corrected covariance.
Scalar conditional categorical-gradient standard errors involving beta kernels now use paired same-observation influences at the two endpoints, including their covariance. Self-contrasts have zero error by construction; point effects, level errors and positive-degree LP covariance are unchanged.
Scalar conditional beta-kernel influence standard errors now use the global sample-covariance factor n/(n-1), correcting an extra division by n in level and supported derivative variances. Conditional point estimates, ordinary regression and positive-degree local-polynomial covariance are unchanged.
Scalar conditional beta-response derivative standard errors now allow a legitimate zero derivative of the explanatory weight sum. Zero endpoint denominators remain invalid; estimates and existing valid derivatives are unchanged.
Scalar conditional Gaussian and Epanechnikov gradient standard errors with fixed/generalized-nearest-neighbour bandwidths now use the squared analytic derivative moment, not a finite-shift convolution constant. This changes uncertainty only; uniform derivative inference and broader adaptive-NN variance questions are not resolved by this correction.
Scalar conditional density and distribution standard errors now use the squared-kernel moments of the chosen explanatory kernels and, for density, response kernels separately. Mixed kernel families/orders no longer inherit the response-kernel moment in every dimension. Point estimates are unchanged.
Pure-categorical density standard errors now use the empirical variance of the complete joint-category kernel contributions, instead of the continuous-density approximation. Constant contributions have zero standard error, and zero-smoothing probabilities retain the existing binomial convention. Density estimates, search, categorical shortcuts, and mixed/continuous density uncertainty are unchanged. The correction reuses existing kernel rows with bounded moment storage.
Pure-ordered distribution standard errors now also use the empirical variance of complete joint CDF contributions. Finite smoothing no longer treats each contribution as a Bernoulli indicator; the zero-smoothing convention, distribution estimates, and continuous-variable formulas are unchanged.
Wang–van Ryzin ordered distribution estimates now use the correct cumulative probability above each training category. This corrects potentially decreasing distribution estimates and affected distribution cross-validation criteria.
Master-local bootstrap and hat fanout computation errors now use the same per-chunk condition handling as workers. Scheduled replies are drained before failure is reported, allowing reuse of a healthy pool without publishing an incomplete result. Scheduling, task seeds and healthy computations are unchanged.
Single-index mean hats and affected bootstrap ratio helpers now normalize finite signed and positive-small weight sums directly instead of flooring them at machine epsilon. Empty external LC/degree-zero index and adaptive conditional-hat rows return NA with one notice; undefined required ratios fail without discarding or replacing bootstrap replications. LL/LP solves, resampling laws, inference formulas and already-correct conditional ratios are unchanged. Weights that underflow before summation are not recovered.
Local smooth-coefficient NOMAD searches now propagate unexpected fixed-bandwidth preparation errors instead of accepting a surviving candidate. Legitimate typed candidate rejection and the distributed preparation protocol are unchanged.
Conditional-density and conditional-distribution fallback probes
now retain the intended physical categorical bandwidths when
bwscaling = TRUE. Valid initial candidates and the existing
penalty/restart rules are unchanged.
Collective smooth-coefficient NOMAD objectives coordinate R preparation and callback-result failures before reporting the original error on every rank. Declared candidate rejection is unchanged; unexpected errors cannot publish a surviving candidate. Native collective unwinds and rank loss are outside this cooperative R-error boundary. Owned-pool evaluation, objectives, trees and the existing adaptive-NN computational owner are unchanged.
External local-linear and local-polynomial fits now report exactly empty computed kernel rows as NA in the undefined output components, with one warning after the caller finishes. Supported components and required training, covariance and bootstrap computations keep their existing policy. Point and asymptotic plot calls collect row notices across their evaluation grids, and vector-tau least-squares quantile predictions report once per completed call. Required bootstrap failures remain terminal.
The root-owned generalized-nearest-neighbor scale pilot for least-squares quantile regression now broadcasts cooperative local failures before raising the original error, allowing the worker pool to be reused.
Single-index fits, predictions and bootstrap row calculations now complete their MPI result exchange before reporting a cooperative local fitting error. The original error is preserved and the worker pool remains reusable.
Single-index NOMAD refinements retain the original physical bandwidth lower bound. A valid incumbent is no longer rejected because its changed index coefficients imply a different scale at the refinement handoff. Direct fixed-degree calls and actual-beta restart scaling are unchanged.
Single-index fixed-bandwidth NOMAD searches reject out-of-domain exploratory bandwidths without terminating the search. Explicit input and final-result validation remain strict, and unexpected callback errors still propagate.
Single-index distributed objective services now coordinate R preparation and result-handling failures before closing the search. Unexpected NOMAD evaluator errors remain terminal instead of publishing a surviving candidate; workers remain available for subsequent calls. Scientific objective arithmetic and normal search budgets are unchanged.
Generalized-NN single-index training summaries and coefficient covariance use a consistent training-evaluation convention when external predictions or different inference outputs are requested. Previously affected serial external-data results are corrected; the ordinary no-external-data convention and MPI counterpart are preserved.
Shared call evaluation now distinguishes returned condition objects from raised errors. MPI worker command loops also accept returned try-error values; actual raised-command failure policy is unchanged.
Coefficient plot controls again validate their values after the dotted-name migration. Single-index objects retain their observation count for vector indices, and NOMAD-only index searches report cumulative evaluations plus the existing final certification count.
Regression fits and plots replay a saved formula data expression in the bandwidth call’s existing environment, so wrapper-local data remains usable after the wrapper returns and after saving and reloading the bandwidth object.
External local-polynomial npreghat mean rows with finite moment systems but exactly zero computed kernel weights now return NA with one informative warning. Defined rows, kernel accumulation, solver arithmetic and internal complete-operator failure behavior are unchanged. The same row contract covers matrix, apply, constraint and recomputed prediction outputs.
Single-index autodispatch now materializes caller-local
se.type values through the shared argument transport owner,
just like se. Literal choices, defaults and uncertainty
calculations are unchanged.
Public plot arguments now use dotted names: data.overlay, data.rug, factor.boxplot, boxplot.outliers, coef.index, common.scale, proper.method, proper.control, boot.control, grid.control and render.control. Plot helpers similarly use bar.num, y.vars, y.dat and pair.list. Retired underscore spellings are no longer accepted. Function names, saved-object fields, defaults and numerical behavior are unchanged; typo hints use the new names.
Single-index starting-bandwidth restoration now shares the active search progress line. Its width-aware notice clears with the search rather than leaving a separate message; search computations and failure diagnostics are unchanged.
Finite-data local-constant single-index hat and plot apply helpers now use shared kernel moments instead of materializing the full weight matrix. Existing bandwidth normalization and denominator floors are preserved; explicit matrix outputs and non-finite-input matrix semantics are unchanged.
Ichimura coefficient covariance now computes its conditional moments and common denominator in one kernel-sum call, preserving covariance formulas and random-number generation. Generalized-NN moments use the mapped MPI training-row owner rather than a master-only kernel-weight matrix.
Single-index fitting now shows one immediate activity line with native heartbeats for fitted values and coefficient covariance. Explicit bootstrap standard errors retain their separate replication progress, without repeated child-regression messages.
Single-index fits now default to
se = TRUE, se.type = "asymptotic", providing asymptotic
coefficient covariance and fitted-value standard errors;
gradients = TRUE also provides gradients and their
asymptotic standard errors. Use se = FALSE to omit all
uncertainty calculations, or explicitly select
se.type = "bootstrap" for the previous fitted/gradient
bootstrap standard errors. Coefficient covariance remains asymptotic in
either mode. Reusing npindex(bws = model$bws, ...) adds
requested outputs without repeating search. Singular
coefficient-information matrices produce an informative error.
Local-constant uniform kernels have zero first derivative and cannot
provide this covariance for free coefficients; use se = FALSE for point
estimates. Default inference adds training derivative and moment work
and can cost appreciably more than a point-estimate-only fit when
bandwidths are held.
Adaptive-NN single-index kernel sums now use donor-bandwidth normalization consistently for fitted values, bootstrap fits and covariance conditional moments, including the shared LC hat/plot helpers. This corrects an existing discrepancy between mean-only and gradient fits; fixed and generalized-NN weighting is unchanged.
Single-index pairs bootstraps reuse the already-computed training index, avoiding a sampled predictor-matrix copy and repeated matrix-vector product per replication. Resampled fits and NN radii are still recomputed. This also removes a one-predictor bootstrap dimension-dropping error.
Single-index bootstrap standard errors now report bootstrap replications, forwarding nested regression-fit activity to the same progress display. Resampling, numerical results and the number of replications are unchanged.
Ichimura single-index coefficient covariance with generalized-nearest-neighbor bandwidths now preserves conditional-moment matrix dimensions when the model has more than two predictors.
Automatic bandwidth calls no longer repeat an entire failed search in other calling frames. Original computation errors propagate normally; namespace-only calls resolve the package selector before execution.
Automatic fixed-bandwidth single-index restarts use the index scale of their drawn beta, with generated h restricted to the existing search bounds. The scale correction can change fresh multistart search results; explicit bandwidths, first starts, RNG draws and the search domain are unchanged.
Kernel-sum formula calls with a subset again allow data to be omitted.
The internal single-index first-start restoration control is documented; npRmpi also transports caller-local values of that control.
Unconditional-density MADS searches decode categorical coordinates for nearest-neighbour bandwidths as well as fixed bandwidths, matching np.
Root-owned smooth-coefficient NOMAD request/reply searches preserve unexpected evaluator conditions, drain the current replies, and stop further evaluator and payload work before existing pool cleanup. Typed invalid candidates retain their handling; collective and no-pool routes retain their existing policy.
Fitted regression releases its owned buffers and partial trees when an error leaves the native fit, preserving the original condition and numerical path.
Explicit na.exclude predictions restore omitted evaluation rows, including native evaluation arguments and composed formula/native omission maps.
LSQ formula dispatch preserves positional subset expressions for the existing formula owner.
Unconditional NOMAD restart reports and selection now use the existing raw certificate while preserving native recovery scores and official diagnostics.
Regression MADS restart reports and selection use their existing raw endpoint certificates while preserving native admission and recovery diagnostics.
Conditional-distribution fixed-degree native restart summaries report their existing raw endpoint scores. Official solver diagnostics retain the score for the official solution; selection and recovery are unchanged.
Conditional-density native restart summaries and selection now reuse the existing raw endpoint score, retaining native-score admission and recovery seed policy. No objective evaluation is added.
Local-polynomial matrix application keeps training and evaluation bases in the same public coordinates when an optional conditioned hat-block attempt returns failure. The existing generic computation reuses the retained source basis; conditioning, solver and failure policies are unchanged.
Fixed-bandwidth single-index R optim searches restore raw-invalid automatic initial bandwidths by at most eight doublings across all supported kernels, orders and regression types, including automatic degree-search children. Each restart retains its own index scale. Valid original starts use the same optimizer invocation without an extra objective evaluation; rejected first scalars are counted and incur no gradient stencil. Explicit or held invalid starts and exhausted restoration fail the call. Later trials, NN bandwidths, NOMAD’s direct search and final certification are unchanged.
Least-squares quantile formula dispatch preserves named subset expressions for evaluation in the formula method’s data mask.
Formula subsets in npcmstest, npqcmstest and npksum resolve data columns before caller-local bindings. Data and subset expressions are evaluated once, including before automatic MPI dispatch.
Pooled conditional-distribution degree searches preserve extended nearest-neighbour bounds when scoring restarts. Searches with multiple starts can now select the better restart under the default extended bounds.
Nearest-neighbour bandwidth metadata retains continuous neighbour counts with either bandwidth-scaling setting, including semiparametric helpers. Conditional bandwidth constructors normalize categorical metadata before validation. Fixed continuous bandwidth scaling is unchanged.
Supplied categorical scale factors are validated in physical kernel units. Fixed-bandwidth categorical optimizer transforms now use the corresponding scale-factor bounds in both directions; physical kernel caps, penalties and optimizer algorithms are unchanged.
MADS/NOMAD+Powell endpoint checks now evaluate the exact returned bandwidths without a round trip through optimizer coordinates. This avoids rounding the point being certified; search coordinates, objective arithmetic and the number of certification evaluations are unchanged.
Conditional-distribution native NOMAD searches rank admissible restarts and publish selected endpoints using their existing raw objective checks. Powell refinement likewise retains its certified endpoint value. No objective evaluations, penalties or recovery-policy changes are added.
Communicator bridge calls now report unavailable storage after MPI finalization instead of dereferencing the freed table. Existing index validation and live-communicator behavior are unchanged.
Regression gradients and their standard errors retain a one-row matrix for a single evaluation point, including categorical asymptotic plot slices; values and multirow output are unchanged.
The MPI local-constant NN gradient adapter preserves the known weighted derivative axes when kernel sums collapse singleton dimensions, including one-point predictions. Kernel sums and derivative formulas are unchanged.
Conditional-density NOMAD degree searches now retain the extended-NN bounds when sending prepared search metadata to workers, so candidate decoding honors the same bounds as the optimizer when extended NN is enabled.
Conditional-distribution cross-validation now rejects failed nearest-neighbour bandwidth preparation before using its output. Zero-radius trial candidates no longer depend on uninitialized memory; valid-candidate objectives and the existing invalid-candidate penalty are unchanged.
MPI communicator rank, size and intercommunicator queries now reject null handles and propagate MPI query failures instead of publishing an uninitialized scalar. Valid communicator results and estimator paths are unchanged.
Conditional categorical bandwidth admission now applies the response and regressor unordered-kernel bounds independently. Mixed Li-Racine and Aitchison-Aitken conditional-density searches no longer reject valid response bandwidths or admit response bandwidths above their kernel bound; objective arithmetic and same-kernel behavior are unchanged. Selected bandwidths and estimates can change when the response and regressor use different unordered kernels.
The embedded wage1 example bandwidth objects were regenerated with current constructors. Their printed bandwidths and derived example results can differ from earlier releases.
Cell and exhaustive degree searches now skip only typed LP-admissibility and NN-candidate rejections. Unexpected evaluator errors propagate with their original condition instead of silently selecting a surviving degree. LP rank/capacity rejection messages and successful search arithmetic are unchanged; failed searches also release their progress display.
Conditional bandwidth transforms and categorical baseline probes now preserve the raw Y-unordered, Y-ordered, X-unordered, X-ordered layout and the corresponding per-side kernel caps. This corrects mistyped coordinates when ordered responses and unordered regressors coexist; objective kernels and the penalty formula are unchanged.
Adaptive-NN local-constant regression overrides now preserve NA at zero-support external rows instead of replacing the fitted value by zero. A single warning identifies the undefined rows; associated requested gradients and standard errors retain NA, matching serial results.
Undefined external smooth-coefficient fits now return NA for the affected fitted values, coefficients and requested standard errors, with one informative warning, instead of publishing an internal finite penalty. Valid rows and the ridge schedule are unchanged. Required training and bootstrap fits still fail when their local systems cannot be solved.
MPI dimension creation now validates scalar counts and dimension-buffer capacity before entering MPI, preventing out-of-bounds access for malformed inputs while preserving valid factorizations and the R wrapper’s coercion.
Growing the MPI request array now first reserves matching status capacity. This prevents bulk request completion from writing past the status buffer after an explicit request-capacity increase; default completion paths and estimator arithmetic are unchanged.
Earlier regression uncertainty documentation clarified the then-current fitted-residual HC0 calculation, derivatives, contrasts and beta kernels. The residual-normalized correction described above supersedes that uncorrected formula.
Formula estimators and regression inference helpers now retain explicitly supplied data values in wrapper calls, instead of looking up their argument names again in the formula environment, including least-squares quantile regression bandwidth and fitted-object formula calls. Formula expressions and retained data behavior are unchanged.
Smooth-coefficient NOMAD bandwidth objects now publish complete bandwidth and scale metadata. This fixes multi-smoother summaries and NA fits or predictions from malformed scalar degree-zero fixed-bandwidth objects, without changing the selected bandwidth, objective or search evaluations.
Automatic MPI dispatch now transports ordinary bandwidth-search controls from their caller’s frame, including solver, bounds-transform, penalty, initialization and smooth-coefficient iteration controls. Local variables and forwarded arguments now retain the same values as literal controls.
Smooth-coefficient bandwidth selection skips redundant scalar zero-ridge correction work, retaining the existing ridge safeguards and results.
Base plots now annotate supported variability intervals
consistently for levels and gradients/effects: individual full-frame
figures retain subtitles even with
options(plot.par.mfrow=FALSE), while package-managed
multi-panel pages receive one shared top annotation. Categorical bars
and multi-quantile overlays use appropriate interval and coverage-scope
wording. Explicit subtitles and existing graphics geometry are
preserved; labels that cannot fit safely are omitted. Class-probability
plots now honor the same global layout option.
gradient.order is the sole supported
derivative-order spelling. The gradient_order
plot/single-index alias is no longer accepted, and gradient accessors
reject that spelling instead of ignoring it. Diagnostics suggest
gradient.order only on routes that support derivative-order
requests.
Mixed fixed/free degree searches now assert after NOMAD returns that every fixed degree coordinate still equals its requested value.
NN recovery now leaves an allowed extended-NN incumbent unchanged and proceeds to the existing fail-closed result instead of reporting an internal ordinary-domain error.
Smooth-coefficient NOMAD evaluation now maps every invalid raw objective to the declared search penalty, independent of bandwidth type, and normalizes the selected bandwidth payload before final certification and publication.
Prepared MPI regression MADS restarts are now admitted only after raw endpoint certification, matching the serial search contract; fixed-degree regression and conditional-density prepared searches also synchronize the current master search options before entering collectives.
Raw-only regression objective certification now applies the same
active continuous scale-factor floor as serial np,
preventing an inadmissible endpoint from being certified only in
MPI.
The attach-mode process-exit finalizer now checks MPI lifecycle state before querying communicators, avoiding a crash after an explicit MPI finalization.
mpi.bcast(..., type = 5) now transfers an incomplete
final chunk separately, preventing reads and writes beyond vectors whose
lengths are not multiples of buffunit.
Source builds on R versions before 4.4 now provide the LAPACK integer type used by compact-support regression checks, while newer R versions retain their native LAPACK type definition.
Long-double entropy-bootstrap work arrays now use R’s aligned long-double allocator, avoiding undefined behavior on platforms that require stricter alignment.
npksum(..., permutation.operator = "integral") now
initializes and independently owns its tree-range workspaces, preventing
invalid memory access and double-free behavior without changing the
integral result.
Gradient standard errors for a single-level ordered predictor no longer read beyond the predictor’s category table, including adaptive-NN dense and tree routes; its identically zero contrast remains unchanged.
Automatic cell-based coordinate/exhaustive polynomial-degree
searches now preserve a configuration error when every degree candidate
fails with the same condition. Missing fixed cker*,
cxker*, or cyker* limits, and missing required
beta-kernel support specifications, now produce the same actionable
messages as NOMAD searches; heterogeneous or numerical candidate
failures retain the generic no-admissible-model error.
NOMAD degree searches now honor mixed fixed/free degree bounds
such as degree.min = c(0, 0) with
degree.max = c(1, 0). Fixed coordinates remain fixed
through starts, restarts, optional Powell refinement, and result
metadata; all-free, all-fixed, and exhaustive searches are
unchanged.
Terminal nearest-neighbor zero-radius errors now share a
diagnostic that identifies the continuous variable and tied-donor
geometry and suggests fixed bandwidths (the default).
npsigtest() also identifies the failing stage. Successful
fits, positive radii, and optimizer trial rejection are unchanged;
repeated values alone are not rejected.
Nearest-neighbour bandwidth searches now reject constant continuous training dimensions before native allocation or collective evaluation, matching the serial input-support contract. Fixed bandwidths are unaffected.
Evaluation-only bandwidth calls now return one initialized history entry, rather than exposing unused entries when more than one start was requested.
Automatic NN search recovery uses a bounded deterministic schedule of ordinary-NN points. It does not exhaust all feasible NN indices; failure to find a valid restart is not a proof of infeasibility. Explicit starts, raw-objective certification and mandatory optimizer refinement are unchanged.
Bandwidth searches no longer reuse large-bandwidth geometry from an earlier dataset. This fixes history-dependent density cross-validation values and protects subsequent searches and regression fits from stale geometry.
Adaptive-nearest-neighbour distribution cross-validation on external grids now gathers all required donor weights across MPI ranks. This corrects allocation-dependent objectives without changing fixed/generalized-NN ownership, the objective formula, or bandwidth geometry.
The proof-of-concept npregiv() and
npregivderiv() estimators now reject unsupported
nearest-neighbour bandwidths early with a clear message. Fixed
bandwidths remain the default and are unchanged.
Formula-created single-index fits now reuse their retained training data for prediction and subsequent evaluation, avoiding spurious missing-variable errors when response and predictor columns are not separate workspace objects.
Principal estimator and bandwidth entry points now reject unknown
named arguments passed through ... before evaluating their
values. Diagnostics suggest an unambiguous canonical spelling when one
is available, while unnamed arguments and established retired-argument
diagnostics retain their existing behavior.
Public bootstrap-count arguments for npcmstest(),
npqcmstest(), npdeneqtest(),
npdeptest(), npsdeptest(),
npsigtest(), npindex(),
npsymtest(), and npunitest() are now
consistently named B, with the historical default of 399.
The former boot.num input is retired; existing
$boot.num result metadata is unchanged.
npsigtest() now displays one immediate,
coordinator-owned progress row with compact predictor names. Individual
tests report predictor-level completion and an ETA after the first test
finishes; joint and single-predictor tests retain bootstrap-level
progress through the existing fanout beacons. Statistical results,
random-number streams, and MPI work partitioning are unchanged.
Eligible individual `npsigtest()` calls with fixed bandwidths and IID residual resampling now evaluate bootstrap statistics in bounded native tiles. The streamed path preserves the incumbent bootstrap samples, random number stream, statistics, and P-values across the serial and MPI paths; unsupported configurations continue through the incumbent R loop.
Regression, conditional-density, and conditional-distribution evaluation rows whose required kernel normalization is exactly zero or non-finite now fail closed locally. Their estimate, standard error, gradient, and gradient standard error components are returned as `NA` where requested, and the corresponding hat row is `NA`, without invalidating defined rows. Every finite nonzero normalization remains literal and unperturbed.
Fitted density and conditional-density log-likelihood aggregates now use the literal logarithm of every represented strictly positive estimate, including subnormal values. Exact zero and negative estimates retain the established computational placeholder; no underflow-recovery owner is introduced.
Density likelihood cross-validation now takes the true logarithm of every represented strictly positive contribution, including positive subnormal values and positive values retained only in signed-log form. Exact zero and negative contributions retain the established guarded mapping.
Leave-one-out regression and conditional objective shortcuts now
preserve every finite nonzero 1 - h_ii leverage denominator
exactly. Near-saturated all-large polynomial designs no longer replace a
positive representable denominator by machine epsilon; zero or
non-finite deletion denominators cede to the established
invalid-objective path.
Continuous generalized- and adaptive-nearest-neighbour bandwidths now use the literal kth admitted distance throughout ordinary, beta-kernel, and kernel-sum owners. Exact ties that make this radius zero invalidate the requested bandwidth instead of silently widening it to the nearest positive distance or replacing it by a numerical floor. Search candidates retain their established invalid-objective mapping, while direct public computation reports the zero-radius condition. Every represented strictly positive radius remains admissible; k, occurrence exclusion, extended-NN scaling, cache policy, MPI partitioning, and public interfaces are unchanged.
Generalized-nearest-neighbor unconditional plot bootstraps now preserve literal resample multiplicities when MPI workers own exact kernel-sum evaluation. This matches serial resampling semantics; adaptive-nearest- neighbor and fixed-bandwidth ownership are unchanged.
Mixed beta-kernel conditional hats now pass the validated categorical- compression state to their private direct kernel-weight owner. This repairs conditional density gradients and simultaneous beta-X/beta-Y fits that previously stopped for both dense and compressed execution; the two modes remain numerically identical across active worker counts.
Copula density helpers now translate the public normalized
ordered Li–Racine kernel name to the private
kbandwidth/npksum spelling. This restores
ordered and mixed copula density semantics without changing public
kernel names, defaults, or MPI orchestration.
Wide fixed-bandwidth local-polynomial
npreghat(..., output = "apply") calls now use the
training-coordinate block shortcut only when evaluation coordinates
actually equal the training coordinates. Equal row counts no longer
misclassify a distinct external evaluation grid; the cutoff, arithmetic
owners, MPI orchestration, and public interface are unchanged.
Partial-linear INID bootstrap coefficient solves now attempt the
identified unregularized weighted least-squares system before entering
their bounded ridge ladder. The former positive 1e-12
default altered every identified replicate and could make an exactly
singular system appear solved before the declared 1/n
stabilization step. Failed zero-ridge solves still enter the existing
1/n, 2/n, …, capped sequence; MPI
orchestration and public interfaces are unchanged.
Partial-linear bootstrap coefficient solves now ridge the Robinson-residualized regressors symmetrically. The former first-coordinate RHS adjustment treated a user regressor as an intercept and made ridged results depend on regressor order; weights, ridge ladder, resampling, and public interfaces are unchanged.
Smooth-coefficient fit and bandwidth ridge corrections now use the finite, nonzero signed pristine moment intercept. This preserves constant reproduction under common moment scaling without changing ordinary zero-ridge solves or the existing bounded ridge sequence.
Ridged local-polynomial response, influence, and R fallback solves now restore the intercept from the signed pristine Gram intercept rather than the already-ridged diagonal or a numerical floor. This restores constant reproduction on rank-deficient systems without changing ridge admission, sequence, cap, diagnostics, or ordinary zero-ridge arithmetic.
Regression bandwidth search now preserves terminal invalidity through finalization and raw-certifies the selected point once outside the optimizer loop. Finite invalid guidance uses the exact criterion-specific constant null for CVLS, the trace-one null for CVAIC, and the Bernoulli null for CVKS; penalty multipliers must be finite and strictly greater than one. Coordinator and worker evaluations use the same raw/finalization contract.
Location-scale quantile bandwidth selection now evaluates leave-one-out check loss against the requested response when its fitted transformed response differs, uses the exact constant-quantile check-loss null for invalid guidance, and rejects an optimizer payload whose selected point is raw-invalid. The chosen NOMAD/Powell semantics are unchanged.
Single-index bandwidth selection now raw-certifies selected Ichimura and Klein–Spady candidates and uses their exact declared leave-one-out nulls for outer invalid guidance. Inner regression evaluation returns raw objective values or terminal invalidity and propagates implementation errors; valid objectives, caches, and optimizer choices are unchanged.
Positive-degree local-polynomial categorical gradients/effects
now report their documented fitted-value endpoint contrasts across
regression, conditional density/distribution, quantile regression,
conditional mode, location-scale quantile regression, and plot/bootstrap
workflows instead of native zero placeholders. Categorical asymptotic
effect standard errors remain unavailable and are reported as
NA.
npsigtest() now chooses pivotal continuous and
unstandardized categorical statistics by default, records the effective
choice, and fails explicitly when requested pivot standard errors do not
define a statistic. Bootstrap values equal to the observed statistic are
included in the empirical upper tail, so a completely smoothed-out
predictor returns P = 1 rather than a false rejection.
Explicit local-polynomial tests require degree at least one only for
tested continuous coordinates. Joint/predictor status now stays on the
existing coordinator-owned live progress line; worker output remains
suppressed.
Successful public bandwidth autodispatch now treats the existing
collective acknowledgement as the publication commit for the already
rank-local lease. This removes one redundant post-evaluation collective
from npregbw(), nplsqregbw(),
npplregbw(), npindexbw(), and
npscoefbw() while preserving their supplied-bandwidth
consumer leases. Scoped internal contexts retain their explicit
collective commit; bandwidth values, objectives, optimizer evaluations,
fitted results, failure rollback, retirement, and public object shape
are unchanged.
Fixed partially linear plot bootstraps now select their
distributed scalar or general local-polynomial owner from canonical
component specifications. Explicit regtype="lp", degree=0
therefore uses the same scalar LP0 owner as public
regtype="lc", instead of being misrouted to a general-LP
state helper that correctly rejects degree zero. Bootstrap arithmetic,
counts, random draws, MPI tasks and collectives, and positive-degree LP
routing are unchanged.
Multi-response npreghat(..., output = "apply")
results now retain the response column names consistently across native,
leave-one-out, and row-local local-polynomial owners, matching the
corresponding hat-matrix application without changing numerical values
or ridge diagnostics.
Local-polynomial response, influence-row, regression-CV,
conditional X-row, and bootstrap owners now share one typed
numerical-rank and ridge policy. Ordinary full-rank response rows retain
the one-call DGESV lifecycle; only an ambiguous
working-precision pivot enters a cold, symmetrically equilibrated
singular-value check. Rank-deficient rows use one Gram-relative
bounded-ridge transcript for response and adjoint solves. This restores
dense/tree objective parity for sparse fixed-bandwidth LP windows and
makes npreghat() matrix/apply results agree with fitted
values on deficient rows without adding evaluation-by-training
storage.
Public regtype="ll" is now exclusively an API and
reporting alias for the canonical raw GLP degree-one engine. Regression
fit, uncertainty, objective, npreghat()
matrix/apply/constraint, single-index hat, conditional X-side, and
plot/bootstrap consumers all select owners from canonical engine
metadata. The retired private local-linear matrix/ridge and single-index
hat routes have been removed. Results can change only where those
private routes formerly selected a different regularized fit; explicit
raw LP degree one and unaffected LC/LP configurations retain their
canonical results.
Fixed local-polynomial plot bootstraps now solve count-compressed
response moments through the same canonical bounded-ridge response owner
used by ordinary regression fits. Regression, single-index,
smooth-coefficient, partial-linear, and conditional density/distribution
bootstrap consumers share one native batched response solve; the former
private determinant, solve(), and absolute-ridge
implementations have been removed. Count construction, moment
arithmetic, chunking, progress, MPI rank assignment, collectives, and
result ordering are unchanged. Results can change for systems where the
retired private path imposed a different ridge decision.
Eligible fixed and generalized-nearest-neighbour LP mean
applications now use the canonical regression response owner for one or
more right-hand sides. A one-column
npreghat(..., output = "apply") no longer falls through a
separate kernel-weight matrix reconstruction that could disagree with
npreghat(..., output = "matrix") %*% y and
npreg() on higher-degree or low-rank local windows.
Adaptive and extended nearest-neighbour routes, beta kernels, derivative
operators, ridge policy, MPI collectives, and public interfaces are
unchanged.
Ordinary univariate generalized-nearest-neighbour regression now
admits k = 1 only for the mathematically certified positive
scalar capability: second-order Gaussian or Epanechnikov kernels with LC
mean/derivative operation, including the equivalent generalized-LP
degree-zero representation. One exact-field capability resolver supplies
manual validation, fixed-degree optimizer bounds, and the native lower
endpoint; an occurrence-aware geometry validator rejects literal zero
radii. Adaptive NN, mixed or multivariate regression, positive-degree LP
(including the public ll alias), other kernels/bounds,
automatic degree domains that can reach positive degree, and
semiparametric owners retain k >= 2. The policy contains
no sample-size, timing, host, rank-count, or acceleration branch and
introduces only linear-in-evaluation geometry scratch.
Rank-local generalized nearest-neighbour regression evaluations
now share one execution-context classifier across npreg()
and npreghat(). This prevents a worker from re-entering a
nested MPI kernel-weight owner merely because the active-pool flag is
coordinator-local, preserving persistent pools across successive kernel
and local-polynomial calls without adding a collective, changing
numerical arithmetic, or introducing size-based dispatch.
Regression leave-one-out hat-matrix and apply routes now share the objective’s occurrence-aware generalized/adaptive NN geometry, source basis, weighted moment layout, factorization, bounded ridge transcript, and intercept restoration. Matrix and apply consume the same completed influence row; apply retains linear-in-sample auxiliary storage and does not construct an evaluation-by-training matrix. The reconstructed owner preserves the August-16 one-call response solve and existing bounded ridge boundary; all-zero compact-kernel LOO rows now fail explicitly instead of returning a fabricated ridged estimate. No new MPI collective is added.
Smooth-coefficient NOMAD degree search now transports its scoped
prepared context through an exact locked opcode token instead of
comparing a serialized function closure by process-local identity. Its
service workers are also stopped before the context lease is retired.
This restores one- and multi-worker npscoefbw() completion
without changing objective arithmetic, search policy, evaluation
accounting, or message count.
Exact adaptive-nearest-neighbour leave-one-out objectives now
construct each donor radius on the literal fold that excludes both the
focal observation and the donor itself. A shared
adjacent-order-statistic owner supplies regression, unconditional
density, conditional density, and empirical conditional-distribution
rows without a per-fold sort or pair matrix. Saturated and extended
counts scale the largest available fold radius by
k/(n - 2); the full-design integrated-square term in
density CVLS retains its distinct full-sample radius and
k/(n - 1) extension. This repairs formerly invalid
high-count objectives and adaptive regression optimizer handoffs while
preserving literal duplicate occurrences, zero-radius infeasibility, and
linear-in-sample auxiliary storage.
Extended generalized-nearest-neighbour training queries now preserve the supplied occurrence identity before applying the package’s linear radius extension. Regression, unconditional density/distribution, and conditional mapped-training owners therefore select the farthest literal neighbour only after deleting the identified focal occurrence. Equal-valued external queries retain external geometry, and ordinary non-extended counts are pointwise unchanged.
Empirical-sample conditional-distribution CV now evaluates the
documented off-diagonal criterion with divisor n(n - 1).
Ordinary generalized nearest-neighbour rows construct the explanatory
radius from the delete-one sample and the response radius from the
occurrence union {i, j} required by
Fhat_{-i}(Y_j | X_i). The exact row and bounded block
owners share one two-slot order-statistic selector, preserve duplicate
occurrences, and add no pair matrix or per-fold sort. Apart from the
separately documented exact adaptive-fold correction, fixed, adaptive,
and beta sample-grid objectives receive only the normalization
correction; genuine external and default response grids are unchanged.
Because the diagonal was already omitted, this changes reported
training-grid criterion values by the common positive factor
n/(n - 1) relative to the former n^2 divisor
and does not change the mathematical minimizer on a common candidate
domain.
Empirical distribution and conditional-distribution CVLS owners
now finalize their shared pair-count normalization through one checked
native seam. Invalid training/evaluation shapes and non-finite
accumulators return the existing infeasible status instead of permitting
a zero pair-count division to produce NaN or
Inf; valid objective arithmetic, MPI reductions, search
domains, and public interfaces are unchanged.
Ordinary generalized nearest-neighbour conditional-density and conditional-distribution training fits now construct explanatory and response radii after deleting the identified focal occurrence, while still retaining that observation in the estimator sum. Scalar and local-polynomial owners share the same explicit occurrence contract and reject literal zero radii. Equal-valued external queries, fixed and adaptive bandwidths, beta kernels, extended nearest-neighbour bandwidths, and external-grid search objectives retain their existing contracts.
Ordinary generalized nearest-neighbour conditional-density CVML now constructs both explanatory and response radii after deleting the focal training occurrence used by each leave-one-out objective row. The scalar, local-polynomial row, and bounded block-stream owners share the same explicit occurrence contract; zero literal radii fail the candidate cleanly. Fixed and adaptive bandwidths, conditional-density CVLS, conditional-distribution external-grid objectives, beta kernels, and extended nearest-neighbour bandwidths retain their existing contracts.
Ordinary generalized nearest-neighbour unconditional density and distribution rows now distinguish identified training occurrences from equal-valued external evaluation points. Training fits and CVML construct each query radius after deleting its focal occurrence, while full fitted rows continue to use every observation in the kernel sum. External queries, density CVLS, adaptive and extended nearest-neighbour bandwidths, and beta kernels retain their existing contracts; a literal zero radius is rejected rather than replaced by a positive neighbour.
Broad-support positive-degree npscoef tree
objectives now retain one rank-local, invocation-owned outer-product
workspace across full-contiguous tree rows. The unchanged design matrix
is packed once while response/weight scratch is reused; sparse, partial,
and multi-segment support retains the incumbent row-local owner. Public
bandwidths, objectives, evaluation accounting, fitted results, and
coarse MPI row ownership are unchanged.
nplsqregbw() and nplsqreg() now support
the package-wide nomad = "auto" shortcut. One continuous
smoothing dimension uses a family-local coordinator that gives each
exhaustive fixed-degree candidate its own complete autodispatch
transaction; two or more dimensions retain the existing NOMAD/Powell
joint search. Historical nomad = FALSE and
nomad = TRUE routes, the shared MPI objective owner, and
the separate logical-only nomad.pilot control are
unchanged.
Conditional-density automatic degree search now provisions
response-side categorical workspace for every engine that a later degree
candidate can activate, rather than only the engine selected by the
initial degree. This restores npcdensbw() and
npconmode() search-trajectory parity across serial and MPI
owners without changing fixed-candidate objective arithmetic, degree
policy, or public evaluation accounting.
Regression bandwidth plots now request standard errors on every direct asymptotic-fit call used by the MPI-aware progress path. This preserves the shared plotting contract for unavailable gradient panels, endpoint geometry, and ordinary formula/session plots instead of failing during interval assembly.
On Apple silicon, fixed-bandwidth compact-support regression-tree CV objectives of local-polynomial basis width six now use compile-time resident sparse-pair and rank-owned-row microkernels. Tree order, coarse MPI outer-row ownership, the unique upper Gram triangle, and the common mirror/solve finisher are unchanged. Widths below and above six are unaffected, and portable builds retain the incumbent generic width-six owner.
Fixed-bandwidth compact-support regression CV candidates whose kernel support covers the complete observed continuous range now use the canonical dense LP owner instead of unproductive full-tree traversal. A shared, operator-aware geometry classifier also routes full-support fixed kernel sums to their dense sibling and searches mixed-support trees only over coordinates whose kernels can prune, while continuing to evaluate every kernel factor. These exact, allocation-free, rank-symmetric policies apply to Apple and portable builds without sample-size or runtime thresholds and without new communication; width-two regression CV, genuinely sparse, nearest-neighbour, non-compact, and unsupported owners retain their established routes.
Conditional-density native degree search now constructs its
finite invalid- candidate penalty once from the same canonical
baseline-and-retry rule as the ordinary bandwidth route and restores
that rank-common value before every candidate. This repairs false
-10000000 NOMAD baselines in npcdens() and
npconmode() while preserving the incumbent vectorized
width-one CVML owner, public evaluation accounting, and linear
rank-local storage.
MPI autodispatch now materializes S3 forwarded arguments
exclusively from the method frame that owns their promises. Named
... controls, explicit NULL, and omitted
arguments retain their identities without scanning unrelated dynamic
frames. This repairs stack-dependent NOMAD multistart transport in
nplsqregbw() and removes its obsolete route-local frame
assignment while preserving the documented public defaults and native
search semantics.
options(np.macMseries.accelerate = FALSE) now
disables the Apple-qualified weighted-design and conditional LP
transpose-GEMV siblings consistently. Ordinary BLAS/LAPACK algebra
remains available independently of this option, and
TRUE/"auto" retain the qualified Apple-arm64
owners.
Generalized fixed-bandwidth local-polynomial CV objectives of basis width six now retain only the unique upper triangle of their symmetric weighted Gram rows and mirror it once before solving. Serial unordered-pair and MPI rank-owned rows share this arithmetic contract; the portable scalar and Apple-arm64 vector siblings avoid the packed-width regression when optional acceleration is disabled while preserving wider, tree, and nearest-neighbour owners and linear-in-sample auxiliary storage.
npconmode() now evaluates categorical response
levels through one memory-bounded conditional-density batch instead of
paying a complete MPI fit lifecycle for every level. Requested
class-probability gradients retain one singleton evaluation for their
selected level while all other levels share the batch. The canonical
rank-owned npcdens() engine, probability repair, class
selection, and result arithmetic are unchanged; temporary evaluation
rows are capped independently of the number of response levels.
Eligible fixed-bandwidth local-polynomial CV objectives now apply the same basis-neutral owner rule to generalized, additive, and tensor bases: resident accumulation through basis width six, packed BLAS from width seven, and the independently qualified generic resident owner for sparse tree rows. The hoisted basis, canonical solve, deletion, projection, loss arithmetic, and MPI row ownership are shared; no basis-family or kernel-spelling fallback remains. Widths five and six retain only the unique symmetric Gram triangle during accumulation and mirror it once before solving.
Fixed-bandwidth compact-support npscoef objectives
now pack each rank-local sparse tree row’s active support into the
existing BLAS outer-product engine for moderate and wide
local-polynomial moment systems. Coarse outer-row MPI ownership is
unchanged, while dense, degree-zero, nearest-neighbour, signed,
derivative, and permutation routes retain their existing contracts and
rank-local workspace bounded by active support and moment-system
width.
Fixed-bandwidth compact-support regression trees now use a
bounded generic resident-row accumulator for local-polynomial basis
widths above five, sharing the canonical solve, deletion, and uncentered
projection engine. The MPI owner retains coarse outer-row distribution,
so explicit np.tree = TRUE and eligible
np.tree = "auto" both use the repaired tree; dense and
smaller-width routes are unchanged.
Fixed-bandwidth degree-zero conditional-density CVLS now recognizes an all-categorical explanatory side at its constant upper kernel limit as the canonical global-X design. This restores the unconditional-density limit, including the ordered Li-Racine case, and avoids redundant conditional X weighting. Generalized/adaptive NN and response-side kernel limits are unchanged.
Degree-zero conditional-density and conditional-distribution CVLS objectives now enter the same canonical all-large explanatory-bandwidth context as positive-degree local polynomials. The width-one case uses its implicit unit basis and scalar Gram inverse, avoiding the dense locally weighted pass while preserving delete-one objective semantics and linear rank-local storage. Conditional-distribution tree rows now translate their independent response permutation once at the shared indicator boundary.
Fixed-bandwidth two-predictor local-linear CV objectives now keep sparse tree rows resident and accumulate the same unique symmetric Gram triangle as the canonical dense width-three engine before using their shared mirror, solve, and uncentered projection. Serial pair enumeration and rank-owned MPI rows share this algebra; MPI still distributes complete outer rows and performs one bounded collective reduction.
Bootstrap-intensive tests now reuse fixed kernel geometry in
bounded, rank-local replication groups. npcmstest() and
npqcmstest() keep one model refit per draw but contract the
resulting residual or score columns through multi-response kernel sums;
fixed, unbounded npdeneqtest() routes process pooled
multiplicity columns while bounded and nearest-neighbour routes keep
literal duplicate resampling; and
npdeptest(method = "summation") uses a bounded native index
batch that shares only the invariant response-side marginal work. Draw
order, complete bootstrap payloads, bandwidth and model semantics, RNG
restoration, progress, rank-count independence, and bounded rank-local
workspace are preserved.
Spawned MPI sessions now finalize the master during an early
process-exit finalizer, after closing and verifying any retained worker
pool, instead of deferring finalization until namespace teardown. Normal
scripts consequently return status zero without external
setsid containment, including soft pool reuse, forced
close, and omitted explicit close. Public pool-reuse and
mpi.finalize() contracts are unchanged; standard tests and
demo launchers now require a clean exit and no longer accept status 137
or broadly kill processes by name.
Fixed-bandwidth fourth- and sixth-order Gaussian convolution rows now hoist their invariant normalization scale out of the observation loop. Conditional CVLS objective values are unchanged while the affected higher-order kernel pass avoids two divisions per row element.
Gaussian entropy integration now hoists validated bandwidth
reciprocals out of its training-by-evaluation kernel loop and evaluates
its three exponentials in bounded vForce blocks on supported Apple
silicon. This accelerates npdeptest() and
npsdeptest(method = "integration") without changing
bandwidths, bootstrap decisions, accumulation order, or asymptotic
memory use; options(np.macMseries.accelerate = FALSE)
retains the scalar route.
Default fixed-Gaussian entropy summation now uses registered,
streamed native owners for npunitest(),
npsymtest(), npdeptest(), and
npsdeptest() on every rank. Univariate and symmetry
bootstraps consume bounded chunks of multiplicities while preserving the
exact incumbent sample.int() or boot::tsboot()
draw plan, RNG state, collective fanout, and reassembly; dependence
statistics fuse their marginal and joint kernel sums without an n-by-n
cache. Explicit generalized- and adaptive-nearest- neighbour requests
remain on literal, duplicate-preserving resample/refit routes so
sample-owned radii and ordered lag pairs are unchanged.
General bounded conditional-density CVLS now reuses each
response-side quadrature kernel tile across a memory-bounded group of
rank-owned explanatory-row tiles. Ordinary and beta kernels share the
same canonical engine; MPI ownership, objective arithmetic, and
accumulation order are unchanged, and storage remains
O(n B) under the existing conditional-LP tile
budget.
Eligible bivariate surface plots now share one base-perspective
frame owner, one viridis palette mapper, and one neutral-gray three-face
grid style across the base and rgl renderers. The shared
renderer is used by the regression, density, distribution, conditional,
semiparametric, conditional-mode, and copula plot families; estimator
and plot-data arithmetic are unchanged.
Internal direct regression evaluators now enter native MPI collectives only when the current call is executing symmetrically on every rank. Master-only fit, prediction, and test helpers remain local even when a slave pool is active, preventing an unmatched collective wait; public auto-dispatched and explicit broadcast regression calls retain their distributed owner and estimator arithmetic.
Prepared local-polynomial conditional-density maximum-likelihood cross-validation now assigns each observation row to one MPI rank and reduces an order-preserving contribution vector once per objective. This removes rank-replicated LP row accumulation while retaining byte-identical objective values and bounded workspace. The incumbent nonprepared route and the beta kernel’s existing distributed row engine remain isolated in their original sibling kernels, avoiding nested ownership and adjacent timing regressions.
Uncertainty migration: npudens(),
npudist(), npcdens(), npcdist(),
npplreg(), npqreg(), npconmode(),
and npcopula() now default to se = FALSE,
joining the existing opt-in controls in npreg(),
npscoef(), and nplsqreg(). This is
intentionally backward-incompatible for scripts that assumed these
uncertainty fields were always populated in earlier releases or
candidate builds. The exception is npindex(): its default
se = TRUE includes asymptotic coefficient covariance;
explicit se = FALSE omits it. Gradients and uncertainty
remain separate requests where supported. For example,
npreg(bws = model$bws, gradients = TRUE, se = TRUE) adds
requested outputs without repeating bandwidth selection; supply the
original training data if it cannot be recovered from the bandwidth
object. Here model is an illustrative object name.
se(), vcov(),
coef(..., se = TRUE) and
gradients(..., se = TRUE), where supported, only extract
stored results and give a no-search refit message when those results are
absent. Explicit inference requests such as
predict(..., se.fit = TRUE) and
plot(..., errors = "asymptotic") request their needed
computation. Omitted internal uncertainty fields can be
NULL or length-zero vectors (for example,
derr = numeric(0)); use the public extractors rather than
relying on one internal empty-field shape. The former Boolean estimation
argument errors remains rejected; plot methods retain their
method-valued errors control. No bandwidth search is
repeated merely to extract stored results.
Conditional density and distribution kernel summaries now
identify explanatory and dependent kernels separately for continuous,
unordered, and ordered variables. Bandwidth-object print()
and summary() methods share one formatter, public output no
longer exposes internal cxker*/cyker* argument
mnemonics, and invalid categorical-kernel selections name the specific
uxkertype, uykertype, oxkertype,
or oykertype argument.
During bandwidth-objective evaluation, MPI-3 builds now use progress-aware nonblocking forms of the existing shared post-kernel collectives. Rank zero can therefore maintain the canonical progress heartbeat while waiting for workers, without changing collective buffers, operations, ordering, or estimator arithmetic. Fit/evaluation routes and MPI-2 builds retain their existing blocking collectives.
Interactive renderer = "rgl" surfaces now use a
high-DPI-aware widget canvas, a higher-resolution uncertainty legend,
and an initial camera more closely matched to the base perspective. The
renderer caps backing-store scaling at two device pixels per CSS pixel,
retains native rgl axis labels and interaction, and preserves all
existing rgl.* user overrides. Shared rgl setup and option
routing now have one internal owner across supported plot families.
Existing band = "all" legends now use the same
construction-aware quantile, rank, and normal-SE labels as the base
renderer, without adding an rgl subtitle or footer; estimator,
bootstrap, interval, and base-renderer arithmetic are
unchanged.
npindexbw() now canonicalizes its numeric design
matrix once at the public boundary and reuses that matrix during
internal index and coordinate setup. This removes redundant full-matrix
copies before objective evaluation while preserving estimator arithmetic
and conditions on every rank.
The integration routes for npunitest(),
npsymtest(), npdeptest(), and
npsdeptest() now use bounded deterministic
corrected-trapezoid quadrature for their default Gaussian statistics.
Univariate iid bootstraps reuse a fixed grid and evaluate bounded chunks
of resampling counts; symmetry bootstraps reuse reflected density
values; and bivariate quadrature calls the registered constant-storage
Gaussian evaluator in bounded tiles. This removes the
cubature dependency while preserving bootstrap sampling
laws, fixed bandwidth reuse, result shapes, seed restoration, nondefault
kernel routes, and existing MPI fanout and reassembly
contracts.
Removed the private, unexported package-local gsl.bs
bridge and its registered native wrappers. Public spline construction
remains owned by crs::gsl.bs(); the independent B-spline
primitives used by the canonical Bernstein local-polynomial engine
remain in place on every rank, with estimator arithmetic
unchanged.
The canonical beta row engine now owns its malloc-backed route, categorical, derivative, and row workspaces behind one unwind-protected cleanup boundary on every rank. Nested derivative helpers borrow that invocation-scoped scratch rather than allocating it per call, so user interrupts and R errors cannot strand native storage. Successful PDF, CDF, convolution, derivative, powered-derivative, and regression arithmetic and rank ownership are unchanged.
The shared regression fit now allocates its operator vector once, after engine and route validation. This removes a duplicate general-path allocation and the scalar beta route’s retained allocation on every rank without changing estimator arithmetic.
Beta nearest-neighbour bandwidth preparation now calls the shared continuous-distance owner directly on every rank. It no longer temporarily rewrites unrelated package-global scaling and categorical factors, while retaining the same generalized- and adaptive-nearest-neighbour arithmetic.
Nearest-neighbour lookup and distance failures now leave both shared bandwidth owners through their normal cleanup exit on every rank. This removes retained standard-deviation and distance workspaces after recoverable failures while preserving successful fixed, generalized- nearest-neighbour, and adaptive-nearest-neighbour arithmetic and conditions.
Removed the unreachable conditional-bootstrap exact-state sidecar and its bypassed MPI worker block. Public conditional bootstrap routes retain their canonical distributed refit/count and fixed local-constant owners, without serial fallback.
Removed the unregistered and callerless internal
beta_kernelsum facade. Beta bandwidth-mode ownership now
resides in the shared bandwidth contract; all estimator, kernel-sum, LP,
optional-output, and distributed routes continue to use the canonical
continuous-row engine.
Higher-order beta convolution now prepares each distinct side/component shape once per observation pair and rank rather than rebuilding it for every opposing component. The stack-local decomposition retains the canonical centered gamma-ratio and signed component-pair accumulation order, changes no order-two arithmetic, and adds no dynamic or sample-sized storage.
Fixed-bandwidth beta CDF rows now prepare each coordinate/component concentration once per invocation and rank rather than recomputing it for every evaluation-observation pair. The fixed-only sibling shares the canonical CDF arithmetic and failure statuses with scalar and nearest- neighbour rows, cannot fall back after selection, and adds only coordinate- by-component transient storage per rank.
Beta CDF rows now prepare observation support coordinates and
higher-order coefficient state once per invocation and rank rather than
rebuilding them for every evaluation-observation pair. Fixed,
generalized-nearest-neighbour, and adaptive-nearest-neighbour
distribution rows share the same canonical pbeta()
arithmetic and failure contract. The prepared route is selected outside
row traversal, cannot fall back to the scalar CDF evaluator, and uses
only beta-coordinate-linear transient storage per rank; density-only
routes retain their existing prepared-PDF path unchanged.
Beta regression gradient rows now form the signed-log level and its regular/jump target derivative from one canonical component preparation on every rank. The level phase still completes before derivative work, preserving native failure precedence and exact fitted, gradient, and standard-error arithmetic while removing the duplicate beta shape and PDF pass. Fixed- and generalized-nearest-neighbour gradient rows additionally reuse their prepared evaluation-row components on every rank. Adaptive-nearest-neighbour gradient rows reuse invocation-owned observation transforms and coefficient state while retaining pair-owned bandwidth shapes and normalizers. Each bandwidth topology selects an isolated sibling before row traversal, with no inner-loop route branch or scalar fallback. Derivative-only fixed/GNN state is allocated lazily on every rank, so level-only fits retain their incumbent preparation cost.
Adaptive-nearest-neighbour beta PDF rows now prepare support-transformed observations and higher-order coefficient state once per invocation while retaining observation-owned bandwidth shapes and normalizers in the pairwise evaluator. This preserves exact kernel-weight, objective, fit, and prediction arithmetic on every rank, uses beta-coordinate-linear transient storage per rank, and cannot fall back to the scalar row after the prepared route is selected.
Fixed- and generalized-nearest-neighbour beta PDF rows now prepare support- transformed observations, component normalizers, and higher-order coefficient logs at their invocation or evaluation-row lifetime instead of recomputing them for every observation pair. The shared row engine retains exact objective, fit, prediction, and kernel-weight arithmetic, has no runtime fallback after route selection, and uses only beta-coordinate-linear transient storage per rank.
Hardened beta self-map layout validation at the registered
kernel-sum, nearest-neighbour bandwidth, and canonical continuous-row
boundaries. Internal calls that identify training and evaluation data
now require equal extents and a strict logical self-map flag, preventing
an inconsistent native invocation from aliasing a shorter training
buffer as a longer evaluation buffer. Valid estimator and
npksum() arithmetic is unchanged.
Corrected automatic-degree regression startup for beta kernels and local-polynomial engine transitions for every continuous kernel. Internal zero bandwidth placeholders are now owned by bandwidth selection rather than validated as manual beta bandwidths, and each evaluated degree selects the scalar width-one or general LP engine through the shared canonical rule on every rank. Returned objectives replay exactly across one- and multi-worker searches without fallback or post-search rewriting.
Corrected native regression NOMAD handoff so the returned bandwidth vector is the exact decoded point evaluated by the resident objective, including mixed predictors stored in non-kernel-grouped column order. The resident search now shares the ordinary regression kernel-state configuration for beta order, categorical compression, and tree eligibility; fixed-degree and automatic-degree results therefore replay their stored objective exactly across local, one-worker, and multi-worker routes.
Corrected fixed-bandwidth beta local-polynomial regression with an active MPI pool so rank ownership retains the canonical scaled beta moment row used by serial and nearest-neighbour routes. The owner path no longer substitutes the legacy continuous-kernel accumulator; fitted values, standard errors, and gradients now agree across local, one-worker, and multi-worker evaluation while retaining bounded row storage.
npseed() now applies its validated C-backend seed to
every rank of an active MPI pool through the canonical command-broadcast
path. This corrects rank-asymmetric multistart state that could hang a
later collective bandwidth search without adding a separate
synchronization protocol. Calls made before pool startup remain local,
while an explicit mpi.bcast.cmd(npseed(...)) continues to
seed once per executing rank without a nested broadcast.
Corrected active-MPI conditional-density derivative hat evaluation so its internal kernel-sum calls carry concrete argument values across the autodispatch boundary. This removes a rank-local lexical lookup failure while preserving the serial arithmetic, warnings, and chunking contract.
Corrected active-MPI derivative kernel-weight output so every
rank initializes non-owned cells before the canonical reduction. Output
ownership is now invocation scoped and each derivative block retains its
own tree-support intervals, also correcting compact-support adaptive-
nearest-neighbour tree output and yielding exact local, one-worker, and
multi-worker p.kw results without additional
observation-square storage.
Corrected positive-degree raw local-polynomial bandwidth
selection for conditional density and distribution estimators when
predictor units make the raw polynomial coordinates numerically ill
conditioned (for example, calendar years). The signed full-row influence
engine now selects one globally equivalent, conditioned coordinate
representation before row traversal and uses it across scalar, block,
tree, and all-large owners. The selection is basis-family neutral, does
not center at evaluation points, and retains only
O(n k + k^2) workspace for basis width k. The
scalar row owner also now reuses the hoisted LP basis for the whole
objective rather than rebuilding and clearing it for every evaluation
row.
Conditional count-bootstrap evaluation with a beta predictor or
response kernel now uses one family-neutral native ingress for
continuous and mixed categorical data, with strict
np.categorical.compress = TRUE/FALSE behavior.
Beta X or Y rows retain the canonical scaled-row owner, while the other
side uses the shared continuous/categorical scalar registry; the
implementation streams O(n + B) scratch and supports an
entirely categorical non-beta side. For generalized- and
adaptive-nearest-neighbour bandwidths, compressed multiplicities are
expanded before row evaluation so bandwidth radii belong to the realized
resample rather than the unreplicated source rows.
Corrected conditional plot/bootstrap dispatch so local-polynomial beta fits retain the declared LP engine on every exact resample instead of being intercepted by the local-constant count evaluator. MPI exact refits execute locally within each outer bootstrap worker, avoiding nested collectives.
Conditional count-bootstrap levels with a beta predictor or response kernel now reuse the canonical scaled conditional rows and contract all count columns through BLAS. The evaluator streams one observation-length row per side and retains no observation-by-evaluation matrix. This preserves the established weighted conditional ratio up to floating-point accumulation order while substantially reducing repeated count-evaluation time.
Conditional-distribution least-squares bandwidth selection with a
beta predictor or response kernel now uses the shared conditional
local-polynomial CVLS engine across scalar and positive degrees, raw and
Bernstein bases, fixed, generalized-nearest-neighbour, and
adaptive-nearest-neighbour bandwidths, and supported mixed
continuous/categorical data. Predictor rows use the canonical signed
delete-one influence solve, while response rows use the canonical CDF
operator on the actual evaluation plane. A bounded response-row
supertile retains O(n B + B^2) workspace and reuses each
CDF tile across small groups of predictor tiles; allocation failure
selects only the algebraically identical linear-memory row stream. The
former private beta conditional-distribution objective and its
caller-free cache helpers have been removed.
Conditional-density least-squares bandwidth selection with a beta predictor or response kernel now uses the shared conditional local-polynomial CVLS engine across scalar and positive degrees, raw and Bernstein bases, fixed, generalized-nearest-neighbour, and adaptive-nearest-neighbour bandwidths, and mixed continuous/categorical data. Predictor rows use the canonical signed full-row solve and exact delete-one identity; response rows retain the established bounded-quadrature or analytic-convolution owners. A bounded analytic-response supertile reuses convolution rows without retaining an observation-square matrix. Automatic Powell, MADS plus Powell, and degree search now accept these supported conditional-density configurations; conditional-distribution bandwidth activation remains separately gated.
Conditional density and distribution gradients with a beta response kernel now retain the same signed-infinite endpoint limits and explicit missing gradient standard errors as the canonical beta regression engine. Finite gradients still use the strict finite restoration path, and indeterminate extended-real scaling remains an error; plot geometry continues to omit non-finite coordinates without mutating returned estimator values.
Corrected internally computed generalized-nearest-neighbour convolution kernel sums so every continuous coordinate owns the required training-point companion bandwidths. The public route now uses the same unequal-bandwidth convolution contract as supplied-bandwidth consumers instead of dereferencing an absent companion matrix; fixed and adaptive bandwidth routes are unchanged.
Corrected conditional-density least-squares cross-validation so its integrated-square and fitted-value terms use the same signed delete-one X smoother in every analytic and categorical-profile route. Adaptive-nearest- neighbour conditional rows now apply their observation-specific X bandwidth divisors and pair response convolutions with the corresponding row bandwidth. A scalar response-row shortcut is now restricted to ordinary density evaluation rather than being reused for convolution. These changes intentionally correct affected CVLS objectives and adaptive-nearest- neighbour CVML and conditional-distribution objectives; bounded-quadrature routes and adjacent fixed/generalized-nearest-neighbour objectives are unchanged.
Beta density bandwidth selection now supports mixed continuous,
unordered, and ordered data for CVML and bounded target-quadrature CVLS,
across fixed, generalized-nearest-neighbour, and
adaptive-nearest-neighbour bandwidths and beta orders 2, 4, 6, and 8.
The continuous beta row and categorical kernels use the canonical
density objective owners, including strict
np.categorical.compress = TRUE/FALSE behavior;
no beta-specific objective or categorical fallback is used. Other
automatic beta bandwidth families retain their existing continuous-only
gates.
Corrected native automatic-degree search for conditional density and distribution bandwidths so every callback selects the canonical scalar degree-zero or general positive-degree local-polynomial engine from that callback’s degree. Returned bandwidth objects now reproduce their stored objectives at the selected degree. Least-squares quantile degree search now updates public and engine metadata atomically, including singleton and Powell-refinement routes, instead of producing an inadmissible first objective from mixed legacy state. Automatic conditional-density callbacks also use a rank-invariant transition-consensus schedule.
Conditional estimators and their gradient, hat, plotting, quantile, mode, and progress helpers now read canonical local-polynomial engine metadata by exact field name and validate the fields jointly. Malformed or mutated bandwidth objects fail with an error identifying the invalid field instead of reconstructing another engine from public display metadata. Derived least-squares quantile objects now retain the canonical engine state of their regression bandwidths.
np.categorical.compress now requires a single
non-missing logical value whenever categorical compression is
applicable. Invalid explicit values fail with a precise option error
instead of silently disabling the selected compression route; valid
TRUE/FALSE behavior and routes where the
option is inapplicable are unchanged.
Fixed-bandwidth categorical conditional-density CVLS now bounds its three predictor, response, and response-convolution profile matrices within one checked workspace: 64 MiB for rank-one execution and 256 MiB for active MPI. Above the ceiling, stable resident rows and dedicated scratch rows are filled through the shared categorical-profile kernel engine. A 32-profile convolution supertile preserves every scalar accumulation order while reusing each response-convolution row across the group, reducing representative objective time without changing objective bytes. Rank-local readiness is agreed before collective traversal, and internal failure after route commitment is terminal rather than a silent fallback.
Fixed-bandwidth categorical conditional-distribution CVLS now bounds its aggregate predictor- and response-profile kernel workspace at 64 MiB for serial/rank-one execution and 256 MiB under active MPI. Problems that fit retain the established dense arithmetic exactly; larger profile topologies use stable reuse-priority resident rows plus one scratch row, filled through the shared categorical-profile kernel engine. Objective accumulation order and values are unchanged, and route readiness and committed failure are rank-symmetric.
Fixed-bandwidth ordered-categorical unconditional-distribution CVLS now consumes compressed kernel rows through a checked, caller-owned tile capped at 64 MiB for serial/rank-one execution and 256 MiB under active MPI instead of retaining the full evaluation-by-training profile matrix. The traversal and loss accumulation orders are unchanged, and objective values remain byte-identical; the general route remains canonical when a single row cannot fit the bounded workspace. Once the bounded route is selected, internal failure now fails rank-symmetrically rather than silently re-entering the general route.
Autodispatch result sanitation now preserves explicit
NULL call arguments without deleting their list slots while
replacing temporary symbols. This corrects a shared post-computation
failure that affected calls such as
npudistbw(..., gdat = NULL, do.full.integral = TRUE) and
could shift or overrun later arguments while reconstructing the returned
call. Estimator arithmetic and MPI work ownership are
unchanged.
Added a dormant, rank-local categorical-profile tile engine for subsequent migration of dense profile consumers. Its caller-owned output is bounded by a checked 64 MiB ceiling, immutable training profiles can be validated once per traversal, and no production estimator dispatch changes in this checkpoint. The same work corrects unordered derivative dispatch so score operators reach their canonical kernels instead of the historical normal- kernel fallback; ordinary objective, fit, and prediction results and MPI ownership are unchanged.
Rank-local Powell-side objective caches now use checked capacity,
key-width, load-factor, and rehash arithmetic under an independent 64
MiB peak ceiling per native table. Growth is transactional on each rank,
and the existing all-rank cache-hit agreement prevents a candidate
cached on only some ranks from bypassing evaluation. Allocation-failure
diagnostics are aggregated once when the search returns, avoiding new
hot-loop synchronization. Cache keys, objective values, optimizer
behavior, and the strict np.objective.cache option contract
are unchanged.
Smooth-coefficient local-polynomial cross-validation and fitting
now solve common stable zero-ridge row systems through one registered
rank-local native entry, reusing a bounded O(p^2 + p)
LAPACK workspace instead of making one R-to-LAPACK transition per owned
evaluation row. Successful wider batches also project their hoisted
basis rows in ascending basis order through one register-local native
entry instead of slicing, reshaping, and dispatching every row in R.
Width-one and width-two explicit solvers are unchanged; if any wider row
in a local batch is non-finite, singular, or ill-conditioned, that
complete batch returns to the established R ridge loop. Estimator
formulas, ridge policy, rank ownership, and collectives are unchanged,
although LAPACK workspace alignment and projection reassociation can
change last floating-point bits.
Completed the migration to one canonical, uncentered
local-polynomial compute engine across cross-validation, fitting,
prediction/evaluation, gradients, standard errors, and hat/apply
helpers. Raw and Bernstein bases now use the same basis-neutral solve
machinery; the obsolete centered and Numerical Recipes matrix engines,
including linalg.c, have been removed in favor of
bounded-workspace BLAS/LAPACK implementations.
Local-constant smoothing and explicit all-zero-degree
local-polynomial smoothing now share the canonical implicit width-one LP
engine throughout conditional objectives, fitting/evaluation,
derivatives, hats, and helper routes. The scalar specialization
constructs no unit basis and performs no BLAS/LAPACK solve;
categorical-only routes continue to honor
np.categorical.compress = TRUE and FALSE.
Public regression-type metadata and estimator definitions are unchanged.
This also corrects adaptive-nearest-neighbor conditional-density CVML
for explicit degree zero: its historical general-LP row omitted
observation-specific X-bandwidth divisors and could differ from the
equivalent local-constant objective.
Corrected conditional-density CVML workspace ownership for
categorical responses when the scalar streaming objective is selected
with multiple continuous predictors. Affected bandwidth searches,
including npconmode() workflows, could previously abort the
R process. Native MADS callbacks for the same scalar route also no
longer allocate or read a nonexistent local-polynomial degree
vector.
Generic local-polynomial CV objectives now pack their immutable response-plus-basis operand once per rank and objective evaluation for eligible full drop-one rows, instead of repacking it for every owned observation. Adaptive-nearest-neighbour, tree, reduced-row, and specialized resident-row routes are unchanged, as are objective values.
Conditional-density and conditional-distribution local-polynomial cross-validation now retain signed higher-order predictor-kernel weights in every delete-one X row on every rank. The obsolete QR route silently discarded negative weights; it has been removed in favor of the canonical signed full-row solve and exact diagonal deletion, with a sign-preserving denominator. This intentionally corrects affected higher-order CVLS and CVML objectives; ordinary second-order results and MPI ownership are unchanged.
Conditional local-polynomial full-row deletion now preserves
every finite, nonzero signed 1 - H_ii denominator exactly
rather than replacing sub-machine-epsilon values by a fixed floor.
Exactly zero or non-finite denominators fail through the existing clean
objective path because the corresponding deleted system has no valid
finite row. Ordinary non-singular objectives and MPI ownership are
unchanged.
Eligible fixed and generalized-nearest-neighbour conditional local-polynomial full-row blocks now reuse the bounded weighted-design BLAS assembly already used by conditional-density CVLS. This accelerates conditional-density likelihood cross-validation and conditional- distribution least-squares cross-validation without changing the scalar width-one, adaptive-nearest-neighbour, non-Accelerate, or allocation- fallback paths. The formulas and MPI ownership are unchanged, but BLAS reassociation can change last floating-point bits.
Adaptive-nearest-neighbour conditional local-polynomial objectives now reuse the same bounded weighted-design BLAS assembly on each rank when the basis has at least four terms. Widths one through three retain their established scalar transcript, as do small-sample, non-Accelerate, and allocation-fallback routes. This accelerates conditional-density likelihood and least-squares cross-validation and conditional-distribution least-squares cross-validation; formulas, rank ownership, and collectives are unchanged, but BLAS reassociation can change last floating-point bits.
Adaptive-nearest-neighbour conditional-density likelihood cross-validation now reuses its rank-local X- and Y-row contexts across observations, matching the existing adaptive least-squares architecture. This removes repeated bandwidth and workspace setup without changing row ownership, reductions, or estimator formulas.
Eligible adaptive-nearest-neighbour conditional-density objectives now cache ordinary-Gaussian response-bandwidth reciprocals in each rank-local row context, replacing repeated vector divisions with multiplications. Non-Gaussian, higher-order, bounded, mixed-response, tree, non-Accelerate, and allocation-fallback routes retain the established implementation. Formulas, rank ownership, and collectives are unchanged, but reciprocal multiplication can change last floating-point bits.
On Apple ARM64, eligible fixed and generalized-nearest-neighbour products of two or more ordinary Gaussian continuous kernels now use one vector exponential of the summed squared standardized distances on each rank. Non-Apple, one-dimensional, tree, bounded, higher-order, adaptive-nearest-neighbour, convolution, score, permutation, and generalized-NN large-bandwidth shortcut routes retain the established implementation. The formulas are mathematically identical but can differ in their last floating-point bits.
Fixed-bandwidth fourth- and sixth-order Gaussian convolution rows now hoist bandwidth- and evaluation-point-invariant polynomial terms outside their observation loop. The isolated helper preserves the established formulas, dimension-product order, and objective bytes; ordinary Gaussian, nearest-neighbour, bounded, score, permutation, and non-convolution routes retain their existing direct implementations.
Corrected canonical local-polynomial CVAIC diagonal restoration and assembly for degrees greater than one, generalized-nearest-neighbor degree-one fitting and standard errors, and generalized-nearest-neighbor conditional-density CVLS convolution-bandwidth ownership. These are intentional numerical corrections and can change affected 0.70-6 results relative to 0.70-5.
Local-polynomial solve retries are now bounded across objective, fit, prediction/evaluation, and hat-matrix paths. Non-finite or unrecoverable systems fail collectively instead of retrying indefinitely, and the fit route releases its native workspace before reporting failure; ordinary successful systems retain their established solve, ridge sequence, and MPI ownership.
Aligned the dedicated plot() methods for
npregiv and npregivderiv with the package-wide
regression plotting vocabulary. They now use gradients,
data_overlay, and data_rug;
npregiv defaults to its structural level with the training
response overlaid, while npregivderiv continues to default
to its derivative. Automatic limits include every active curve, response
overlay, and training-support rug, without replacing available fit-time
evaluation curves. The experimental plot-only controls
plot.data, deriv, and phi have
been removed and now fail with migration guidance. Plotting remains
master-side; estimator objects, MPI execution, and numerical results are
unchanged.
Conditional density and distribution level plots now support bootstrap intervals when the continuous X and Y sides use different kernel families or orders, including beta on either side. Beta-containing bootstrap levels use the same signed log-domain numerator/denominator accumulation as the fitted estimator; this also corrects same-family higher-order beta bootstrap values when a finite negative or extremely small explanatory kernel sum was previously replaced by a positive machine-epsilon guard. Fixed, frozen generalized/adaptive nearest-neighbour, and exact nearest-neighbour refit semantics are covered. Bootstrap replicates remain partitioned across MPI workers, and each worker evaluates its assigned counts locally without nested MPI dispatch. Matching legacy-kernel paths, estimator fits, gradients, bandwidth selection, and public defaults are unchanged.
Aligned npregiv() and npregivderiv()
summary output with npreg(). Fixed smoothing now uses the
canonical Kernel Regression Estimator: description
(Local-Constant, Local-Linear, or the
local-polynomial degree and basis details) instead of separately
printing the legacy p, regression type, and degree fields.
This is a reporting-only change; fitted objects, smoothing choices, and
numerical results are unchanged.
MPI result simplification now applies one explicit scalar-logical
contract at master-side ingress. Vector, empty, and missing
simplify values are rejected before remote execution,
parallel apply/replication, or simulation work is dispatched, preventing
rank divergence and an unusable worker pool. Scalar logical values and
numeric 0/1 retain their established results
and MPI transport paths.
npksum() now validates its public logical controls
and kernel.pow before MPI or native dispatch. This prevents
malformed scalar controls from being masked by short-circuit evaluation
and prevents empty, missing, non-finite, or vector-valued kernel powers
from reaching C; an empty kernel power could previously terminate the R
process. Documented scalar inputs and valid integer powers retain their
established behavior.
Repaired npksum() score and OCG output for mixed
data. compute.score=TRUE now allocates its native result
buffer, and packed score, OCG, and continuous permutation blocks
(including derivative kernel weights) are returned in original
data-column order. Categorical base-kernel factors are now retained in
mixed-data continuous permutation blocks, and score-only ordered kernels
no longer dereference OCG-only state. MPI ranks now also assemble their
disjoint derivative kernel-weight slices before returning
p.kw, including uneven fixed/generalized-NN partitions and
adaptive-NN training-row partitions. Uneven fixed/generalized-NN
partitions now also use exact per-rank receive counts, preventing
padding from one packed p.ksum block from overwriting the
first element of the next. The defects could previously cause a
process-level crash, fail during R reconstruction, or return an
incomplete mixed-data derivative or derivative-weight array.
Corrected Landweber–Fridman state coherence in
npregiv(): state N now means exactly
N completed updates, and phi.mat[, N],
norm.stop[N], norm.index, the returned curve,
derivatives, and optional weights refer to that same state. This can
change the selected iteration and returned estimate. Bandwidth replay is
now boundary-safe at norm.index = 1 and retains recomputed
stopping diagnostics. Documented numeric starting.values
now initialize a complete level-and-derivative state.
Corrected multivariate npregiv() derivatives to
return one named pure coordinate partial per continuous structural
coordinate. Local-constant Tikhonov derivative weights now use the
derivative operator and bandwidth for the requested coordinate instead
of combining coordinates or recycling bandwidth divisors. Univariate
Tikhonov results are unchanged, and serial, one-worker, and multi-worker
routes share the corrected contract.
Separated npregivderiv() training and evaluation
state. A fit-time zeval/formula newdata grid
now affects only phi.prime.eval and its path; training
operators, residuals, centering, stopping, selected state, fitted
values, and training gradients remain unchanged. Arbitrary positive grid
sizes are supported, while a different weval is rejected
because the inverse problem is defined on the training instruments. The
default Issue 57 local-linear training route is numerically
unchanged.
Hardened the IV public surface in parity with np:
ordinary IV regression stages now own bandwidth.divide,
ukertype, and okertype without
duplicate-argument failures and use kernel-appropriate categorical
search bounds; derivative seeds, scalar controls, summaries, accessors,
and plots now follow the documented contracts. MPI autodispatch now
fingerprints all tagged remote arguments before reusing a worker
reference, so modifying a returned bandwidth or IV replay object locally
cannot silently reuse stale worker state. Post-fit
predict() remains deliberately unavailable.
The omitted regression-smoothing choice in
npregivderiv() is now local linear
(regtype = "ll", degree one), matching the longstanding
p = 1 default of npregiv(). Explicit
regtype = "lc" reproduces the former omitted-default
computation, while regtype = "lp", degree = ... remains
available. Local-polynomial order applies only to continuous predictors;
categorical-only internal stages use the equivalent local-constant
route. This intentional default change can alter derivative paths,
stopping states, and fitted structural functions for calls that
previously omitted regtype.
npregivderiv() now forwards user-supplied unordered
and ordered categorical regression kernels to its internal regression
stages without colliding with the private Equation (14) adjoint. The
adjoint continues to own its required Li–Racine categorical kernels and
ordinary-CDF normalization; previously successful calls and estimator
defaults are unchanged.
npregivderiv() progress now identifies the
mathematical object being computed, including E[y|w],
initialization derivatives, conditional residuals, and the derivative
adjoint T*{E[y-phi(z)|w]}. State-zero work is distinguished
from work at completed iteration N; estimator results,
stopping behavior, smoothing choices, and MPI lifecycle are
unchanged.
Modernized the public npregiv() and
npregivderiv() interfaces in parity with serial
np while preserving their established numerical defaults.
Both now support explicit IV formulas (y ~ z | w, with
optional | x where the estimator supports it),
data, subset, na.action, fit-time
newdata, fixed
regtype = "lc"/"ll"/"lp" and
scalar degree controls, structured summaries, and
fitted(), gradients(), and training-row
residuals(). Objects retain compact bandwidth, smoothing,
and stage metadata, and the formula calls remain rank-symmetric under
MPI autodispatch. Legacy p remains supported by
npregiv() with unchanged default behavior. Unsupported
post-fit prediction, derivative x, and automatic-degree
NOMAD routes are documented and fail explicitly instead of being
silently accepted or approximated.
Beta associated kernels now interpret using outer half-spacing bounds based on the two smallest and two largest distinct training values. This keeps every observation strictly inside the fitted support, removes empirical-extremum jumps and infinite raw-extremum derivatives, and preserves tied-extremum multiplicity. Explicit fixed beta bounds remain literal, while every non-beta range route continues to use exact sample minima and maxima.
Beta regression hat matrices, leverages, and matrix-free applications now use the same canonical common-scaled row and local-polynomial solve owners as fitted values. Orders 2/4/6/8, fixed and nearest-neighbour bandwidths, raw/Bernstein bases, derivatives, and mixed categorical predictors are supported without reconstructing underflowed absolute weights. Direct application retains only a bounded influence-row block rather than an evaluation-by-training matrix; a full matrix is allocated only when the caller explicitly requests one.
Beta range resolution now validates distinct-extrema metadata coordinate by coordinate. This fixes an unreleased 0.70-6 development defect in which two or more continuous beta coordinates failed during support resolution because vector and scalar logical operators were mixed.
Automatic univariate second-order beta range searches now certify fixed- bandwidth density CVLS and distribution CDF solutions against the double-precision uniform-limit objective. A material improvement triggers one deterministic Powell refinement from the resolved support width, and the best material candidate is retained. The user’s ordinary search and setting are unchanged, and cold starts and restarts from bandwidth objects share the same contract; summaries report the additional certification evaluations. Explicit fixed bounds, CVML, higher beta orders, nearest-neighbour modes, multivariate searches, and non-Powell solvers are unchanged.
Corrected recursive fitted-value centering in
npregivderiv() so both empirical terms of the Equation (14)
adjoint use the same fitted conditional-residual vector. The recursive
path had instead centered its second term on the raw residual even
though its first term used the fitted residual. Serial and MPI
autodispatch routes share the corrected contract. The
npregiv() and npregivderiv() examples now both
use n = 500, for which the documented seed produces stable
estimates.
Corrected Landweber-Fridman state indexing in
npregivderiv(). Iteration N now consistently
denotes N completed derivative updates: column
N of phi.prime.mat and phi.mat,
norm.stop[N], num.iterations, and the returned
derivative/function now identify the same state. The initialization
remains state zero, and evaluated stopping-rule overshoots remain
available in the iteration matrices while num.iterations
identifies the selected state. This can change the selected iteration
and estimates because the stopping criterion is now evaluated as
N times the residual norm at state N, as
described by Florens, Centorrino, and Racine. Serial and MPI
autodispatch routes share this contract.
Corrected the empirical adjoint in npregivderiv() to
use the ordinary kernel CDF required by Equation (14) of Florens,
Centorrino, and Racine. The integral kernel sum had retained an extra
continuous-bandwidth factor, which could make the Landweber-Fridman
stopping norm increase and bias the recovered conditional mean. The
private adjoint now owns its required normalization, while public
npksum() defaults, regression argument forwarding, and MPI
payloads remain unchanged. The iteration guard also examines only the
computed prefix of the preallocated stopping vector. This mirrors the
serial np repair and resolves issue #57.
Retired the unused experimental truncated-Gaussian continuous
kernel and its nptgauss() configuration helper. The
supported continuous kernels are Gaussian, Epanechnikov, and uniform;
their public interfaces, native codes, numerical behavior, and MPI
payloads are unchanged.
Corrected heterogeneous generalized local-polynomial (GLP)
construction to include every coordinate-capped term through the
declared total degree. For example, degree = c(2, 1) now
includes the x1*x2 term. Raw and
bernstein.basis = TRUE GLP fits now use exact
representations of the same complete polynomial space; the latter uses a
deterministic degree-graded, orthonormal shifted-Legendre
representation. Raw heterogeneous GLP results can therefore differ from
earlier versions when terms are restored; multivariate
bernstein.basis = TRUE GLP and automatic degree-search
results can also differ because the former fixed-degree Bernstein
columns did not span the corrected GLP space once interactions entered.
Univariate and maximum-degree-one GLP retain the same polynomial space,
although their floating-point paths can differ. Dimension guards,
higher-order derivatives, fitted values, standard errors, and the
serial/MPI native evaluators use the same term definition. Local
constant, local linear, univariate, additive, tensor, and
non-local-polynomial semantics are unchanged.
Hardened native NOMAD observer and interrupt handling. Explicit
user interruption is now reported as an R interrupt
condition only after native cleanup; active MPI work defers a master
interrupt until the current rank-common computation boundary so workers
remain reusable without adding MPI commands or payloads. Local contained
observers preserve and re-signal R errors after cleanup; distributed
observers retain separate handling. Native callables are resolved per
solve rather than retained across package reloads, and the declared R
3.5 compatibility floor is preserved.
Restored timely master-only progress updates during native NOMAD
bandwidth and degree searches, including long compiled objective
evaluations. Iteration, current degree, and accepted-best details now
follow the package-wide progress interval without changing optimization
results, evaluation accounting, MPI commands, or payloads. This requires
crs 0.15-46 or later.
Restored partial mixed-degree local-polynomial gradient
evaluation and plotting across regression, conditional
density/distribution, least-squares quantile regression, conditional
quantiles, and conditional modes. A continuous derivative requested
above that coordinate’s fitted degree is retained as NA and
shown as an empty panel while other supported derivatives and
categorical first-difference effects continue to plot. Requests with no
available component now fail before bootstrap or MPI work, with the
affected predictor orders and degrees and actionable refitting guidance.
MPI commands and payloads are unchanged.
Corrected common-scale uncertainty-band rendering for partially
available gradient plots. Empty derivative panels now retain their list
positions and finite-range calculations no longer emit spurious
min/max warnings; supported estimates,
interval bands, and MPI payloads are unchanged.
Corrected the heteroskedasticity-robust Ichimura
index-coefficient covariance returned by
vcov(npindex(..., gradients = TRUE)) when the model has
more than one free index coefficient. Link-gradient and residual weights
are now applied observation by observation. Fits with one free
coefficient remain exactly unchanged, as do beta, bandwidth, objective,
fitted-value, residual, and gradient results. Serial and MPI results
remain exactly equivalent, with no payload change.
Repaired finite-support bounded continuous-kernel normalization for all supported Gaussian, Epanechnikov, uniform, and truncated-Gaussian kernels. The centered normalization now avoids tail cancellation, uses the platform C99 error function where appropriate, and preserves the uniform-density limit as the bandwidth tends to infinity.
Replaced the bounded distribution operator with the mathematically correct observation-centered truncated-kernel CDF. Analytic centered interval primitives and hoisted invariant work preserve exact support endpoints, numerical accuracy, the finite-support large-bandwidth uniform limit, and serial/MPI numerical equivalence.
Corrected transform.bounds = TRUE initialization for
regression initial starts and multistarts and for later
conditional-distribution multistarts. External bandwidth starts are now
inverse-mapped before transformed Powell search; the public default
remains FALSE and MPI payloads are unchanged.
Clarified optimizer summaries so objective-cache lookups, NOMAD
point lookups, family-native R-optim refinement, and total
function-evaluation accounting are labelled consistently without
implying that their denominators are interchangeable.
Clarified bandwidth-object replay documentation across estimator families. Stored bandwidth objects retain search and fit metadata but do not silently materialize or expand training data; callers must provide data again where the documented replay route requires it.
Hardened MPI lifecycle and protocol handling. Bootstrap fanout timeout or failure now reports a hard failure and instructs the user to restart R before further MPI-backed computation; attach close resets pool state, profile communicator metadata remains distinct, and internal protocol tags are allocated portably.
Standardized the public nomad.opts contract across
supported bandwidth selectors and hardened native NOMAD callback cleanup
and allocation guards without changing MPI message payloads.
Refreshed MPICH build and runtime guidance for current MacPorts layouts and documented the supported session, attach, and profile/manual-broadcast launch modes. Open MPI remains outside the validated 0.70-5 backend matrix.
Reworked npindexbw() / npindex()
internals after 0.70-4. Ichimura and Klein-Spady single-index objectives
now reuse the established npreg leave-one-out backend where
applicable, preserving the public estimator contract while materially
improving high-dimensional and local-polynomial objective evaluation.
MPI-aware objective services now keep the root-owned optimizer and
worker evaluation protocols explicit.
Repaired MPI single-index NOMAD/Powell handoff behavior for
npindex(..., nomad = TRUE) and related bandwidth searches.
The Powell refinement stage now uses the same one-active-protocol
discipline as the validated MPI search routes, avoiding worker/master
desynchronization during the NOMAD-to-Powell transition.
Distributed post-CV npindex() fitting, evaluation,
gradients, and related components across owned evaluation rows in active
MPI sessions while keeping the full training data available where kernel
smoothing requires it. Focused large-sample checks showed the expected
scaling improvement and preserved serial-equivalent numerical
results.
Added user documentation for npindex() optimizer
choice and beta interpretation. The documentation now gives practical
guidance on when derivative-free Nelder-Mead remains a reasonable
low-dimensional default and when BFGS is useful for higher-dimensional
index searches, and it explains the relative interpretation of
normalized single-index beta coefficients.
Hardened single-index fit, evaluation, plotting, service, and summary behavior. Bounded continuous-kernel options are now carried consistently through objective, fit, evaluation, variance, and bootstrap routes; Klein-Spady confusion-matrix output is guarded against out-of-range fitted values; and large-bandwidth shortcuts are restricted to kernel/order combinations whose constant-weight approximation was validated.
Repaired single-index plot bootstrap memory usage. Wild-bootstrap
self-maps now avoid public-facing O(n^2) kernel-weight
allocations for the ordinary large-sample plot route, while preserving
public fitted/plotting contracts and MPI row ownership.
Repaired generalized-nearest-neighbor local-polynomial derivative
ownership across npreghat() and related public routes.
Mixed-degree local-polynomial fits now route available derivative
components through the correct owner, preserve
apply == H %*% y contracts, and report unavailable
derivative components consistently rather than silently applying the
wrong operator.
Repaired local-constant derivative ownership in
npreghat() so degree-zero local-polynomial derivative
requests use the analytic local-constant derivative contract only when
that contract is mathematically available. Scalar, matrix, multi-column,
serial-equivalent, and active-pool error routes were validated
separately.
Improved conditional mixed-degree local-polynomial gradients for
npcdens() / npcdist() and public
conditional-gradient accessors. The partial-availability contract now
matches the regression-family policy: available components are returned,
unavailable components are represented as NA, and
incoherent metadata fails clearly.
Repaired MPI conditional-density native derivative workspace handling so no-gradient and gradient calls can be mixed safely in one session without stale native derivative workspace state affecting later calls.
Repaired exact nearest-neighbor bootstrap parity in MPI conditional-density and conditional-distribution plot routes. Adaptive and generalized nearest-neighbor exact bootstrap paths now rebuild bandwidth state on the expanded resample consistently with the serial package.
Repaired formula/data reentry contracts across density,
distribution, conditional density, conditional distribution,
single-index, smooth coefficient, partially linear, quantile,
conditional-mode, copula, and significance-test routes. Explicit
estimator data= now overrides stored bandwidth-object data
where that public call shape is supported, formula newdata
is validated against fitted RHS variables, and direct formula calls with
numeric smoothing parameters no longer misroute formula objects as
native data before autodispatch.
Hardened MPI autodispatch argument transport and bandwidth fingerprints. Named arguments materialized through S3 dispatch are preserved before worker fanout, and bandwidth-object reuse checks are stronger against stale remote object reuse.
Repaired unconditional density and distribution edge contracts. Ordered kernel-code selection is now consistent between conditional bandwidth selection and fitting where normalization is required, all-NA input is rejected before native calls, training and evaluation omission metadata are retained separately where needed, categorical zero-bandwidth standard-error handling is consistent across categorical configurations, and unsupported bandwidth-selection method codes fail clearly.
Clarified normal-reference bandwidth documentation for density and distribution routes. The rule-of-thumb formulas are documented as fast exploratory Silverman-style heuristics, not production substitutes for cross-validation or likelihood-based selection.
Hardened npcdistbw() normal-reference method
handling so the R method code and native C method code stay aligned,
avoiding accidental fall-through to an unintended bandwidth-selection
branch.
Repaired and hardened public estimator contracts found during
adversarial audits of npscoef, npplreg,
npqreg, nplsqreg, npconmode,
npcopula, npreg, npcdens,
npcdist, npudens, and npudist.
Repairs include npscoef iterated backfit behavior and MPI
service-task handling, partially-linear fit reentry, quantile
inversion/clamping contracts, conditional-mode probability/tie handling
and orchestrator localization, copula sample reentry, regression
tree/large-h predicate alignment, and density/distribution
formula/native argument consistency.
Plot-bootstrap memory hardening now covers single-index,
conditional-mode, and partially-linear plot routes. The default plot
evaluation grids remain linear in the training sample size, and explicit
neval == ntrain style requests remain
user-controlled.
Native shadow-object and cache lifecycles were hardened. Conditional-density shadow pointers, native objective-cache state, and regression large-h / large-lambda caches are cleared at the appropriate top-level lifecycle boundary so pointer-keyed helper state cannot leak across independent calls, datasets, or MPI sessions.
npksum() numeric-bandwidth dispatch now constructs
data-aware bandwidth objects for mixed continuous/categorical data
instead of falling back to a default-typed object. The serial and MPI
packages now share the same public behavior for numeric smoothing
parameters.
The local-polynomial regression CVKS low-support objective path now uses a named bandwidth-method code and aligns the R and C routing contracts, reducing the risk of drift between search metadata and native objective handling.
Expanded focused tests, demos, benchmarks, and release-protocol sentinels across the public exported surface. The release protocol now requires an explicit public-exported-surface inventory, estimator-family sentinels, documentation/demo/benchmark smoke coverage, MPI scaling canaries, and installed package proof before release-ready claims.
Hardened proactive C cleanup paths by aligning LAPACK helper
out-of-memory cleanup with the serial package, clearing extended
nearest-neighbor alias state in the shared estimator cleanup helper,
routing selected bandwidth-constructor allocation and unsupported-method
failures through existing cleanup labels, and guarding large
np_kernelsum() allocation-size products before calling the
legacy alloc_vecd(int) allocator.
Added nomad = "auto" for local-polynomial degree
searches, preserving the serial package policy while keeping MPI
autodispatch and NOMAD shadow protocols explicit. Small one-dimensional
degree lattices use exhaustive Powell-style search; larger or explicitly
requested NOMAD surfaces continue to use NOMAD.
Repaired npRmpi fit-wrapper NOMAD guards after
normalized NOMAD control handling so nomad = TRUE and
nomad = "auto" routes stay on the intended local/native
path rather than being accidentally autodispatched.
Bandwidth and fit summaries now report cumulative search diagnostics more clearly. NOMAD cache output distinguishes repeated point lookups avoided by NOMAD from true objective computations; Powell summaries expose repeated objective lookups avoided by the package-side cache; hybrid NOMAD+Powell and exhaustive Powell timing labels are reported consistently.
Base-graphics plot scaling and legends have been hardened for
multi-panel displays. Plots now honor active
mfrow/cex behavior more consistently, use
role-appropriate legend sizes, and draw factor legends with point glyphs
matching the plotted estimates.
Fixed-bandwidth bias-corrected bootstrap plot intervals have been reworked across the supported serial-equivalent and MPI-aware plot families. Bias-corrected centers and intervals now share a common centering contract for regression, unconditional density and distribution, conditional density and distribution, single-index, partially linear, and smooth-coefficient plots where supported.
Pair/block/geometric bootstrap intervals in regression-style
routes now use smooth-bootstrap bias correction when
center = "bias-corrected" is requested; wild-bootstrap
regression intervals retain the standard wild-bootstrap correction.
Density and distribution routes use perturbation-based smooth-bootstrap
bias correction. Gaussian, uniform, and second-order Epanechnikov
perturbation kernels are supported; higher-order signed perturbation
kernels fail closed.
Bias-corrected plot support now covers mixed-data unconditional
density and distribution, mixed-data conditional density and
distribution, and conditional gradient displays.
Non-fixed/adaptive/generalized bandwidth bias correction and empirically
unsupported npqreg bias correction remain fail-closed with
explicit messages.
Bias-corrected plot payloads returned by
output = "data" and output = "both" now
consistently expose fitted values, bias-corrected values, gradients,
gradient bias corrections, and interval payloads with the same centering
contract used for rendering.
Derivative-order validation has been tightened across plot/gradient routes so unsupported derivative requests fail early or are represented consistently rather than silently plotting a lower-order derivative.
Single-index formula dispatch with explicit bandwidths has been repaired in MPI-aware routes, and single-index bias-corrected plot centers, output payloads, and legends have been tightened.
The native loadNamespace("crs") call construction
used by CRS-backed native search routes is now protected, resolving the
RCHK protection finding without changing the native NOMAD search
contract.
Added MPI-aware nplsqreg()/nplsqregbw()
support for location-scale quantile regression, including formula/data
and bandwidth-object workflows, scalar/vector tau,
prediction, residual extraction, summaries, and plot routes built on the
shared quantile plotting engine.
Supported MPI MADS/NOMAD-backed bandwidth-search routes now use
the final native crs NOMAD C API rather than the retired
legacy snomadr() fallback. The runtime dependency on
crs is now declared in Imports, while
LinkingTo remains for the native header.
Native NOMAD routes now preserve progress best-record reporting, expose cache/evaluation diagnostics, honor explicit start and option controls, and reject unsupported or indeterminate cache-off settings before solver entry. Inadmissible GLP degree candidates are guarded before expensive evaluator work in serial-equivalent and MPI-dispatched routes.
npindexbw(..., method = "ichimura", regtype = c("ll", "lp"))
now reuses the established local-polynomial regression objective
evaluator, and MPI autodispatch uses a rank-0-driven objective service
for the fixed-degree and NOMAD degree-search Ichimura local-polynomial
routes. Focused sentinel runs preserved payloads while restoring useful
scaling for the formerly flat local-linear and local-polynomial
single-index rows.
MPI fanout coverage has been extended for computationally heavy bootstrap workloads in specification, dependence, distribution-equality, quantile, and symmetry tests, and plot-bootstrap RNG streams now restore the serial-equivalent final state after MPI fanout.
The shipped npplreg attach-mode demo now explicitly
finalizes the master rank after successful attach shutdown, hardening
release-sentinel teardown without changing estimator or runtime
defaults.
MPI auto-dispatch for nplsqreg() now materializes
named method-level ... arguments before worker fanout,
preserving user-supplied scale and option values that
arrive through S3 ..n placeholders.
options(np.tree = "auto") is now the default tree
mode. In auto mode, continuous kd-tree routes are enabled only for
bounded-support continuous kernels ("epanechnikov" and
"uniform"); np.tree = TRUE remains the
explicit force-on override and np.tree = FALSE remains the
force-off diagnostic path.
Powell bandwidth searches now expose package-side
repeated-candidate objective caching through
options(np.objective.cache = TRUE/FALSE). The cache remains
enabled by default and is scoped to one bandwidth solve, so it can reuse
exact candidates across Powell restarts without carrying state across
datasets or later calls. Continuous-only generalized/adaptive
nearest-neighbor routes also retain their integer nearest-neighbor
objective cache under the same switch. The option is synchronized to MPI
workers in autodispatch sessions; NOMAD solver caching and extended-NN
distance reuse remain separate mechanisms.
Continuous large-bandwidth shortcut evaluations can now be
disabled with options(np.largeh = FALSE), and discrete
near-upper-bandwidth shortcut evaluations can now be disabled with
options(np.largelambda = FALSE). Both remain enabled by
default and are synchronized to MPI workers in autodispatch sessions.
These switches are intended for diagnostic timing and reproducibility
studies that need to separate tree effects from large-bandwidth and
large-lambda fast paths without changing the canonical dense/tree
objective machinery.
Local-polynomial regression cross-validation now uses a leaner
hot symmetric weighted-sum loop. Fixed-bandwidth
npregbw(..., regtype = "lp", bwmethod = "cv.ls") objective
probes in active MPI sessions match serial np objective
values to numerical precision while substantially reducing
local-polynomial CV evaluation time.
Shared weighted outer-product accumulation in
npksum() now uses a guarded BLAS dgemm route
when the operation is dense, non-permuted, and memory-bounded. Focused
fixed-bandwidth probes preserve serial/MPI objective parity while
substantially accelerating high-basis local-polynomial regression and
smooth-coefficient objective rows; small and scalar routes remain on the
established loop path.
Unconditional density least-squares cross-validation now uses a
leaner fixed-bandwidth Gaussian convolution loop. Fixed-bandwidth
npudensbw(..., bwmethod = "cv.ls") objective probes
preserve objective values exactly in the focused validation rows while
materially reducing the convolution portion of the objective
calculation. Conditional-density least-squares objective probes inherit
the same fixed-bandwidth Gaussian convolution improvement.
Non-Gaussian scalar-bandwidth convolution helpers now hoist the response bandwidth power outside the inner loop, improving fixed-bandwidth least-squares density cross-validation with compact-support kernels while preserving objective values exactly in focused probes.
Continuous-kernel vector helpers now reuse the loop-invariant signed inverse bandwidth scale inside their inner loops. Focused density, conditional density, and regression objective probes preserve serial/MPI objective parity while reducing repeated scaling work in shared C hot paths.
Conditional density and conditional distribution least-squares cross-validation now use a size-aware row-block policy for local-polynomial objective evaluation. The accepted route keeps the bounded-quadrature cap unchanged, bounds transient memory by sample size, and preserves objective values to numerical precision while materially reducing evaluator overhead for fixed-bandwidth CVLS probes in serial and MPI sessions.
Local-polynomial conditional density maximum-likelihood
cross-validation now uses the same bounded-memory block machinery for
fixed and generalized nearest-neighbor bandwidths. Focused
npcdensbw(..., bwmethod = "cv.ml", regtype = "lp") probes
preserve objective values and selected bandwidths to numerical precision
in serial and MPI sessions while reducing objective and full-search
runtime.
Large-sample categorical-only regression now uses the MPI-safe
profile-compressed route under
options(np.categorical.compress = TRUE), which is enabled
by default. This categorical route is independent of
options(np.tree). Repeated predictor profiles are
compressed before bandwidth search, fitting, prediction/evaluation,
standard errors, hat-helper use, and plot bootstrap helpers, preserving
the established dense-route numerical contract while reducing repeated
work.
Categorical-only unconditional density routes now use the same
profile-compression idea when
options(np.categorical.compress = TRUE) is enabled. The
fixed-bandwidth fit/evaluation route preserves dense-route
fitted/evaluation values while avoiding repeated computation over
identical categorical profiles, and the bandwidth-search route now uses
the same compressed support representation for all-categorical data. As
with other flat categorical search surfaces, selected smoothing
parameters may drift by optimizer-path amounts while preserving the
objective scale. Very fast compressed routes may remain overhead-floor
limited, so MPI acceleration is most useful once the uncompressed work
would be genuinely long-running.
Categorical-only conditional density and conditional distribution
bandwidth searches now honor
options(np.categorical.compress = TRUE). The promoted route
preserves the objective value to numerical precision while allowing
harmless optimizer-path drift in selected smoothing parameters,
especially near upper-bound or large-bandwidth regions where the
objective is flat.
Ordered-only unconditional distribution bandwidth search and
fit/evaluation routes also use profile compression when
options(np.categorical.compress = TRUE) is enabled. The
bandwidth-search route preserves the objective value to numerical
precision while allowing harmless optimizer-path drift in selected
smoothing parameters; fitted distribution values and standard errors are
preserved while avoiding repeated computation over identical ordered
profiles.
Fixed-bandwidth local-constant npscoef() fits now
use categorical-profile compression when all Z variables
are categorical and options(np.categorical.compress = TRUE)
is enabled. The route preserves fitted means, coefficient surfaces,
asymptotic mean standard errors, and coefficient/gradient standard
errors for training and evaluation fits while avoiding repeated work
over duplicate Z profiles. The corresponding
npscoefhat(output = "apply") path and count-based
plot-bootstrap helper use the same profile compression without changing
the explicit full-matrix output = "matrix"
contract.
Internal categorical-profile and large-bandwidth caches are now
cleared at the relevant top-level density, distribution,
conditional-density, conditional-distribution, and regression cleanup
points. These caches are keyed by call-local row pointers, so clearing
them per .Call prevents stale same-process state from
leaking across unrelated data sets or MPI dispatch modes.
Fixed npcdens() and npcdist() formula
calls with explicit numeric smoothing parameters, such as
npcdist(y ~ x, data = dat, bws = c(.25,.25)), so
npRmpi preserves the established
formula-to-bandwidth-object rewrite before MPI autodispatch.
Hardened the npudist() formula route so formula
calls are handled before MPI autodispatch.
npplreg() now activates the already validated
categorical regression compression path for its internal all-categorical
Z regressions when
options(np.categorical.compress = TRUE) is enabled, without
requiring users to request continuous kd-tree acceleration through
options(np.tree).
Formula variables whose names contain dots, such as
y.irr ~ x, are no longer mistaken for the formula wildcard
. in conditional density and conditional distribution
bandwidth routes. The conditional-density bandwidth formula route also
now expands the actual wildcard form y ~ . using the
supplied data frame, matching the conditional-distribution
route.
Fixed MPI conditional-density and conditional-distribution NOMAD
degree-search routes so Powell refinement and promoted wrappers such as
npconmode() reach the intended bandwidth-object
construction path rather than the pre-search autodispatch preflight used
by non-degree-search routes.
npqreg() is now a fully fledged MPI-aware
quantile-regression front end. It supports the formula/data workflow,
internally computes npcdistbw() bandwidths when a bandwidth
object is not supplied, accepts scalar or vector tau,
reuses selected bandwidths for additional quantiles in
plot(), and exposes the usual S3 surface:
fitted(), predict(),
predict(..., se.fit=TRUE), se(),
gradients(), summary(), print(),
quantile(), and plot().
npqreg() prediction now honors the standard
newdata workflow while preserving native exdat
precedence for compatibility with existing npRmpi call
surfaces. Formula-based prediction validates that new data contain the
required right-hand-side variables.
npqreg() plotting has been expanded for vector
quantiles, level/gradient displays, ordered predictors, user-specified
legends, and object-fed plotting of additional tau values
without recomputing cross-validation. The fixed-bandwidth gradient path
now uses the MPI-aware helper route.
npconmode() is now a first-class conditional-mode
estimator. It supports formula/data and bandwidth-object workflows,
forwards bandwidth-selection options to npcdensbw(),
propagates local polynomial and NOMAD metadata, and exposes
fitted(), predict(), summary(),
print(), gradients(), and plot()
methods.
npconmode() now supports optional class-probability
matrices and level-specific probability gradients. For
non-local-constant fits, probabilities are normalized to be non-negative
and to sum to one across the discrete response support before modal
classification.
npconmode() now fails early for non-categorical
responses and validates formula-based newdata against the
original right-hand-side variables.
npconmode() plotting now supports object-fed
class-probability slices and two-dimensional probability surfaces,
optional rgl rendering, and probability-level asymptotic
intervals where defined. Surface bootstrap intervals for class
probabilities remain intentionally deferred.
npcopula() is now a first-class copula estimator. It
supports formula/data and bandwidth-object workflows, automatic
two-dimensional probability grids, explicit u evaluation
grids, and ordinary extractable object components including
$bws.
npcopula() now provides fitted(),
predict(), predict(..., se.fit=TRUE),
se(), summary(), print(),
as.data.frame(), and richer plot() methods.
Plotting supports base persp, image, and
optional rgl rendering, with asymptotic and MPI-fanned
bootstrap intervals for copula surfaces where defined.
npcopula() explicit-grid evaluation now uses the
direct estimator route, preserving numerical results while avoiding the
severe runtime growth of the previous expanded-grid path when users
request larger probability grids.
The automatic local-polynomial NOMAD controls have been split
into explicit restart toggles: powell.remin for Powell
restarts and nomad.remin for the second NOMAD hot start.
This preserves the Powell Numerical Recipes restart default while
allowing NOMAD hot starts to be controlled separately.
Deprecated legacy remin remains accepted by
npregbw() and npreg() with a warning and is
mapped to the modern powell.remin/nomad.remin
controls where appropriate, preserving downstream compatibility while
documenting the new spelling.
Hat-operator helpers now support an additional constraint-oriented output route for objects needed by shape-constrained quadratic programming workflows, avoiding reimplementation of local-polynomial hat-matrix construction in user examples.
Local-polynomial derivative support has been broadened across the
conditional estimator family. npreg(),
npcdens(), and npcdist() now honor
gradient.order more consistently for fitted, evaluated,
predicted, and plotted objects when the selected polynomial degree is
high enough, including vector derivative orders over continuous
predictors and tensor/additive/Bernstein local-polynomial bases. The MPI
implementation dispatches the corresponding conditional hat-apply helper
work across the active worker pool where applicable.
Core and semiparametric S3 prediction paths have been hardened
around newdata, native evaluation-argument precedence,
formula RHS validation, and se.fit handling while
preserving npRmpi route independence.
Front-end/bandwidth argument hygiene has been tightened so
estimator-only controls such as proper are not forwarded
into bandwidth selectors that do not accept them.
MPI lifecycle and plotting routes received additional hardening,
including soft npRmpi.quit() behavior, local object-fed
plot computation where required, and explicit fanout of applicable
bootstrap workloads.
Documentation has been refreshed for the promoted
npqreg(), npconmode(), and
npcopula() workflows, including the local-polynomial NOMAD
route, probability/gradient outputs, plot controls, and examples that
use the streamlined interfaces.
The pre-release validation suite was expanded with focused hostile argument tests, S3 contract tests, installed/tarball proof scripts, route-aware MPI probes, and serial/MPI parity checks for the newly promoted estimator families.
The default multistart cap for bandwidth selection now follows
min(2, p) across the mirrored estimator families, replacing
the older min(5, p) cap. This includes automatic LP
degree-search calls when search.engine="nomad" or
"nomad+powell" and nmulti is not supplied
explicitly.
The univariate boundary density helper
npuniden.boundary() now defaults to
nmulti=1.
The empirical studies supporting this mirror change are
documented in np-master/benchmarks/validation/, with a
summary note kept in this repository’s
benchmarks/validation/ folder.
LP-capable front ends now accept nomad=TRUE as a
documented convenience preset for the recommended automatic NOMAD
local-polynomial route, mirroring the serial package defaults and
help-page guidance.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.