The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

np 0.70-2

• npqreg() is now a fully fledged 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 np 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.
• 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 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.
• Core and semiparametric S3 prediction paths have been hardened around newdata, native evaluation-argument precedence, formula RHS validation, and se.fit handling.
• 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.
• 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, and cross-package parity checks for the newly promoted estimator families.

np 0.70-1

• The default multistart cap for bandwidth selection now follows min(2, p) across the core 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 change are documented under benchmarks/validation/.
• LP-capable front ends now accept nomad=TRUE as a documented convenience preset for the recommended automatic NOMAD local-polynomial route. Missing settings expand to the same long-form LP/NOMAD defaults documented in the bandwidth help pages, and regression formula calls such as npreg(y ~ x, nomad = TRUE) now carry that shortcut through the internally computed bandwidth path.

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.