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Resubmission addressing CRAN feedback on vignette build time (Uwe Ligges, 2026-08-23): “Please reduce the vignette build timings … Otherwise we cannot afford checking the vignette regularly on CRAN.” No change to the statistical methods or to the user-facing API.
The vignettes now use smaller toy data sets and fewer resampling
iterations. Total knit time for the four vignettes drops from 77.5s to
14.5s on the development machine (5.3x), and
checking re-building of vignette outputs inside
R CMD check --as-cran drops from 89s to 26s (3.4x). That
step took 376s on the CRAN incoming check, so the expected saving there
is around four and a half minutes. The source tarball also shrank from
1.35 MB to 1.09 MB.
The dominant cost was a single chunk in brs-intro.Rmd,
45.1s of the 77.5s total: a bootstrap with ci_type = "bca"
on 1000 observations. BCa obtains its acceleration constant from a
leave-one-out jackknife, so that call performed 1000 model fits for the
jackknife on top of the 100 bootstrap replicates. The sample size for
that vignette is now 250 and the replicate count 30.
Other reductions: sample sizes in brs-intro.Rmd (1000 to
200/250), brs-advanced-workflows.Rmd (260 to 150, and the
mixed-effects example from 1200 to 250 observations) and
brs-mm.Rmd (5 groups of 200 to 12 groups of 20, which is
also a more natural design for illustrating random effects); bootstrap
replicates (80/100/120 to 30); marginal-effect simulation draws (120/160
to 60); and cross-validation repeats (5 to 2). All vignettes still knit
without warnings.
brs-advanced-workflows.Rmd computed its bootstrap
twice (once for the table, once again with ci_type = "bca"
for the forest plot) and its average marginal effects twice. Both are
now computed once, with keep_draws = TRUE, and the plots
reuse them. Dropping the redundant BCa call also removes its
leave-one-out jackknife. BCa remains demonstrated in
brs-intro.Rmd.
?brs_bootstrap gains a section on the cost of
ci_type = "bca". The leave-one-out jackknife behind the
acceleration constant requires R + n model fits rather than
R, so the run time is governed by the sample size rather
than by the number of replicates. This was not documented, and it is
easy to hit unexpectedly on a large sample.This release makes no change to the user-facing API. It improves the numerical conditioning of the interval-censored likelihood, removes an unused C++ backend, and collects packaging and documentation cleanups for CRAN submission.
P(lo < Y < hi) that
underlies every interval-censored observation is no longer always
computed from lower-tail beta CDF values. When both endpoints lie in the
upper tail (lo + hi > 1, common when the fitted mean is
close to 1) the difference is now taken between upper-tail (survival)
probabilities, so both terms stay small and the subtraction no longer
suffers catastrophic cancellation. The two forms are identical in exact
arithmetic; the new one is strictly better conditioned in floating
point.man/brsmm_loglik_eigen.Rd
and man/brsmm_group_modes_eigen.Rd, that documented
brsmm_loglik_eigen() and
brsmm_group_modes_eigen(). Those objects do not exist: the
Eigen entry points are registered as the internal
.brsmm_loglik_eigen and
.brsmm_group_modes_eigen. R CMD check reported
both as code/documentation mismatches..Rbuildignore that
had been committed by mistake and was being shipped in the source
tarball, where R CMD check flagged it as a hidden file with
a non-portable name.DESCRIPTION no longer sets LazyData: true.
The package ships no data/ directory, so
R CMD build was already reporting “Omitted ‘LazyData’ from
DESCRIPTION”.betareg was removed from Suggests. It is
not used by any function, test or vignette; the package is only
mentioned in prose when describing the output style of
summary().src/Makevars and src/Makevars.win no
longer request the OpenMP compiler and linker flags
($(SHLIB_OPENMP_CXXFLAGS)). No translation unit in
src/ contains an OpenMP directive, so the flags added
portability risk without any parallelism.TODO.md, a development-only file, is now listed in
.Rbuildignore.NEWS.md records under 2.7.0 the extension of
int_method = "aghq" and int_method = "qmc" to
multivariate random effects, which had been implemented but never
announced. The 2.6.8 heading, which had been concatenated onto the end
of the 2.6.9 entry, is now a separate section.README.md: the S3 interface table marked
autoplot() as unavailable for brsmm objects
although autoplot.brsmm() is registered, exported and
documented; it is now marked as available, and the missing
plot() and extractor (logLik(),
AIC(), BIC(), nobs(),
fitted(), formula(),
model.matrix()) rows were added.README.md: the score-probability example described its
output as “500 patients” while the accompanying simulation creates
1000.README.md: the installation section claimed the package
was “currently under review for CRAN”. It has been on CRAN since 2.6.9
(published 2026-02-25), so install.packages("betaregscale")
is again presented as the primary route, with the GitHub install offered
as the development version.README.md: the package summary and the mixed-effects
section described the random-effects likelihood as Laplace-only,
omitting the AGHQ and QMC methods.README.md: fixed the interval-censoring notation, which
rendered as a semicolon-separated list rather than a closed
interval.?betaregscale) showed its
“Useful links” section twice and listed the maintainer a third time
below the author list. The cause was a block in
R/autoplot.R that used the "_PACKAGE" sentinel
purely to emit @rawNamespace directives, which made
roxygen2 treat it as a second package-level documentation block; it now
uses @noRd and contributes only the NAMESPACE directives
(NAMESPACE is unchanged). A hand-written
@seealso/@author pair in
R/betaregscale-package.R that duplicated what roxygen2
already derives from Authors@R, URL and
BugReports was removed.DESCRIPTION now lists the GitHub repository in
URL alongside the pkgdown site.\url{} in the \references section of the 22
help pages that carry it. The UFPR institutional repository hosting it
is down (502 across the whole server, with an official maintenance
notice), so the link resolved to an error. The identifier is persistent
and the reference unchanged; only the hyperlink markup was dropped, and
it will be restored once the repository is back.src/loglik.cpp
(betaregscale_loglik_mixed_laplace_cpp(),
betaregscale_group_modes_cpp() and the
build_group_index() / group_Q() /
golden_max_group() / laplace_group() helpers,
267 lines). It was reachable from no R code, test or vignette:
brsmm() uses the Eigen backend exclusively.
RcppExports were regenerated and the two corresponding help
pages removed.This is a maintenance release focused on correctness, numerical robustness, and performance. It carries the fixes from a deep audit of the C++ backends and the R interface. No user-facing API changes.
residuals() and the diagnostic
plots for both brs and brsmm). Negative
discrepancies are now handled via sqrt(abs(...)) rather
than being silently truncated to zero.predict.brs() now detects a variable-dispersion model
from the model’s term labels instead of the number of parameters, fixing
incorrect predictions for some fits.sqrt link for the precision submodel now clamps the
linear predictor to be non-negative, preserving the correct gradient
sign during optimization.brsmm) is substantially
more robust: the Newton-Raphson step is accepted only when it improves
the objective, the Hessian regularization is now proportional to the
smallest eigenvalue of -H, and non-finite proposed steps
are rejected before evaluation, preventing
NaN/Inf crashes.(1e-9, 1 - 1e-9) to
avoid infinite values at the boundary.brs() and brsmm().stats::make.link()
objects on every call: apply_inv_link() uses direct
closed-form formulas, and a new apply_link() provides the
forward transform, used across the fitting, methods, and plotting
code.brs_check() is fully vectorized (no R-level loop) and
brs_prep() uses vapply() with a vectorized
NA guard.compute_start() uses a moment-based
estimate for the precision intercept (avoiding a second GLM fit), and
brsmm() initializes the random-effect standard deviation
from the between-group variance.brs_coef() now formally signals its deprecation via
.Deprecated("brs_est").vcov.brs() warns when it cannot compute a finite
covariance matrix (e.g., when MASS is unavailable for the
generalized-inverse fallback).src/brs_common.h) to
remove duplicated code between the Armadillo and Eigen backends, and
documented the numerical tolerance constants.src/Makevars and src/Makevars.win no
longer define -DARMA_NO_DEBUG, enabling Armadillo bounds
checking..brsmm_loglik_eigen entry point is
exported with a leading dot to keep it out of the public namespace.brsmm() now supports the "aghq" and
"qmc" integration methods with
multivariate random effects. Previously both were
restricted to a single random-effects dimension and brsmm()
raised an error for random = ~ 1 + x | group unless
int_method = "laplace". The Eigen backend now builds a
Cartesian adaptive Gauss-Hermite grid and prime-based multidimensional
Halton sequences of the required dimension, so all three integration
methods are available for any random-effects structure.
int_method = "aghq" the grid has
n_points^q_re nodes; brsmm() stops with an
informative message if that exceeds 500,000, suggesting a smaller
n_points or int_method = "qmc".int_method = "qmc" supports up to 50 random-effects
dimensions.autoplot.brsmm() and autoplot.brs() gain
three new arguments:
theme: accepts any ggplot2 theme object or function
(default ggplot2::theme_minimal()), replacing the hardcoded
theme in all 8 internal brsmm and 4 internal
brs plot helpers.title, xlab, ylab: override
individual plot labels via ggplot2::labs().type = "all": renders every available panel in a single
gridExtra::grid.arrange() grid.ncol: controls the number of columns when
type = "all"....: passes additional named arguments to
ggplot2::theme() on top of the base theme.autoplot.brsmm() adds type = "shrinkage":
scatter of Laplace posterior modes versus naïve per-group logit-mean
deviations, with identity line and loess smoother.autoplot.brsmm() updates
type = "ranef_caterpillar": error bars now show ±1.96 ×
Model SD (marginal standard deviation from the D covariance
matrix).plot.brsmm() adds two new base R diagnostic panels:
which = 7: Q-Q normal plot of random-effect posterior
modes.which = 8: dotchart caterpillar of posterior modes
(ordered by value).brsmm_re_study() now returns $icc:
intraclass correlation coefficient on the latent logistic scale
(σ²_b / (σ²_b + π²/3)).print.brsmm_re_study() now displays a VarCorr-style
table (Std.Dev., Corr) and the ICC alongside the existing shrinkage and
normality diagnostics.print.summary.brsmm(),
print.brsmm(), print.summary.brs(), and
print.brs() now apply cosmetic name cleaners that strip
internal prefixes such as (phi)_ from precision
coefficients and convert Cholesky-factor internal names (e.g.,
(re_chol_logsd)_X|g) to readable logSD.X|g /
cov.X:Y|g forms.autoplot.brs and
autoplot.brsmm) now map linewidth = n for
geom_line() instead of the deprecated size
aesthetic, eliminating the ggplot2 ≥ 3.4.0 deprecation warning.tests/testthat/test-re-and-autoplot-improvements.R with 15
new tests covering: .pretty_phi_names(),
.pretty_re_names(), brsmm_re_study() ICC and
VarCorr output, plot.brsmm() panels 7 and 8,
autoplot.brsmm() and autoplot.brs()
title/xlab/ylab, theme arg (object and function),
type = "all", ... forwarding.\value, \seealso, and
\examples{\donttest{...}} tags to multiple S3 method
documentation files (print.summary, residuals,
summary, vcov, ranef) to ensure
full CRAN policy compliance.vignettes/brs-mm.Rmd).ranef() usage in vignettes to correctly call
the generic function.README.md to
be fully compatible with GitHub Markdown, and updated
pkgdown site build configuration to load
betaregscale appropriately during vignette setups.README.md references.brsmm
(mixed-effects) objects to mirror the interface of brs
(fixed-effects) objects:
formula(),
model.matrix(), and confint().residuals() to support conditional
"deviance", "rqr" (randomized quantile
residuals), "weighted", and "sweighted"
options.predict() to support conditional
type = "quantile" evaluations directly.ranef() generic and ranef.brsmm()
method to extract random-effect modes.brs_gof() and
brs_est() to compute GOF properties and estimates directly
from both brs and brsmm objects
respectively.print.brsmm() to explicitly display mean,
precision, and random-effect coefficient blocks side-by-side, mirroring
the verbose visual style of print.brs().@examples audit: added
runnable \donttest{} examples to all ~30 previously
undocumented exported functions, including all S3 methods for
brs and brsmm objects (coef(),
vcov(), logLik(), AIC(),
BIC(), nobs(), formula(),
model.matrix(), fitted(),
residuals(), confint(),
predict(), print(), summary(),
ranef(), anova(), plot(),
autoplot()).set.seed() calls from
examples across 15+ files (fit.R,
brsmm.R, bootstrap.R, cv.R,
marginaleffects.R, scoreprob.R,
table.R, simulate.R, prepare.R,
autoplot.R, autoplot-brsmm.R,
loglik.R). All examples now use deterministic toy
datasets.\dontrun{} anywhere: all examples
are either direct or wrapped in \donttest{} as
appropriate.10.1080/0266476042000214501) added to every occurrence of
that reference across betaregscale-package.R,
brsmm.R, methods.R,
anova-methods.R, and
brsmm-random-effects-study.R.@seealso cross-links added to all S3
method documentation blocks for both brs and
brsmm objects.brs_coef() documentation updated with
deprecation notice, @description, @return, and
@seealso.brs_hessian() documentation improved:
added @param object, @seealso, and a
deterministic example.print.brsmm_re_study() now has a
complete roxygen2 block including @description,
@param, @return, @method,
@seealso, and @examples.ranef() generic now includes
@param, @return, @seealso, and
@examples.autoplot.* examples updated to use
ggplot2::autoplot() (explicit namespace) for reliability in
check environments.\value documentation to print.brs()
and print.summary.brs() methods.\dontrun{} with \donttest{} in
brs_gof() example and created complete executable
example..GlobalEnv modification from
brs_bootstrap() (CRAN policy violation).set.seed() calls from exported functions:
brs_bootstrap(), brs_marginaleffects(),
brsmm(), and brs_cv(). Users must now call
set.seed() externally before these functions for
reproducibility.seed parameter from all four functions listed
above. Documentation updated with recommended usage pattern.logLik(), coef()) per CRAN policy.brs_bootstrap() with
ci_type = "bca" (bias-corrected and accelerated intervals),
plus Monte Carlo diagnostics for interval endpoints
(mcse_lower, mcse_upper).wald_lower,
wald_upper) to bootstrap output for direct asymptotic vs
resampling comparison.autoplot.brs_bootstrap() support to visually
compare bootstrap and Wald intervals in
type = "ci_forest".autoplot.brs_marginaleffects() with three views:
forest, magnitude, and dist.brs_marginaleffects():
keep_draws = TRUE.brs_cens() output to include richer summary
fields (percentage, severity,
interpretation) and optional domain-agnostic interpretation
messages via inform = TRUE.brsmm() to support multivariate random effects
in the mean predictor, including random intercept + random slope
specifications such as random = ~ 1 + x | group.brsmm_group_modes_eigen() to compute posterior
modes of group random effects for general random-effects dimension.anova.brs() and
anova.brsmm() for likelihood-ratio workflow across
brs and brsmm candidates.brsmm_re_study() and
print.brsmm_re_study() for numeric random-effects
diagnostics (covariance/correlation, shrinkage, normality checks).predict.brsmm(), vcov.brsmm(), and
print.brsmm() to support both scalar (q_b=1)
and vector (q_b>1) random-effects structures.D),
ranef, random-effects studies, and prediction
behavior.README.md and vignettes with explicit
multivariate mixed-model mathematics, Laplace formula in matrix form,
and end-to-end model-selection examples.https://doi.org/...) to keep CRAN URL checks robust.brs-intro,
brs-analyst-tools, brs-mm) with stronger
mathematical exposition, explicit likelihood pieces by censoring type,
and clearer inferential interpretation for analysts.brs_bootstrap(),
brs_marginaleffects(),
brs_predict_scoreprob(), brs_cv(), and
brs_table().knitr::kable(..., digits = 4) for better readability
and reporting consistency.pkgdown) to keep articles and reference pages synchronized
with the current API.brsmm() by refining the
optimization control and starting values.simulate() method to better handle edge cases
in random effects simulation.methods.R for better compatibility with
downstream packages.pkgdown site build failures._pkgdown.yml to reflect new vignette
names.brsmm() for mixed-effects beta interval
regression with Gaussian random intercepts
(random = ~ 1 | group) using Laplace-approximated marginal
likelihood..brsmm_loglik_laplace_cpp() and
.brsmm_group_modes_cpp().brsmm objects:
print, summary, coef,
vcov, logLik, AIC,
BIC, nobs, fitted,
predict, and residuals.test-brsmm.R with mixed-model fitting and
prediction tests.brs_table() to compare one or more fitted
brs models in a single table with logLik,
AIC, BIC, pseudo-R2, and censoring
composition.brs_marginaleffects() for average marginal
effects in the mean or precision submodel, with optional
simulation-based uncertainty intervals.autoplot.brs() with ggplot2
diagnostics for type = "calibration",
type = "score_dist", type = "cdf", and
type = "residuals_by_delta".brs_predict_scoreprob() to obtain predicted
probabilities on the original integer score scale.brs_cv() for repeated k-fold cross-validation of
brs models with fold-level predictive metrics
(log_score, rmse_yt, and
mae_yt).pkgdown) to
expose the new analyst-oriented tools.README.md and vignette content with examples
for model comparison, marginal effects, and score-probability
predictions.brs_sim_var() is no longer exported.
Variable-dispersion simulation is now done through
brs_sim() using a two-part formula (for example,
~ x1 + x2 | z1 + z2).brs_loglik() and brs_loglik_var() are now
internal helpers and are no longer part of the user-facing API.brs_sim() is now the single simulation entry point for
both fixed- and variable-dispersion models, with formula semantics
aligned to brs().brs_prep() consistency warnings are emitted once per
call on final prepared output, improving test stability and warning
capture behavior.brs_ prefix for consistency and
ease of typing.
betaregscale() -> brs()betaregscale_fit() ->
brs_fit_fixed()betaregscale_fit_z() ->
brs_fit_var()betaregscale_loglik() ->
brs_loglik()betaregscale_loglik_z() ->
brs_loglik_var()betaregscale_simulate() ->
brs_sim()betaregscale_simulate_z() ->
brs_sim_var()prepare_data() -> brs_prep()check_response() -> brs_check()censoring_summary() -> brs_cens()beta_reparam() -> brs_repar()gof() -> brs_gof()est() -> brs_est()hessian_matrix() -> brs_hessian()betaregscale_coef() -> brs_coef()betaregscale has been renamed to brs. All
associated S3 methods have been updated accordingly (e.g.,
summary.brs, plot.brs).type argument removed: The
deprecated type argument has been completely removed from
all functions: check_response(),
prepare_data(), betaregscale(),
betaregscale_fit(), betaregscale_fit_z(),
betaregscale_loglik(),
betaregscale_loglik_z(),
betaregscale_simulate(),
betaregscale_simulate_z(), and internal helpers
compute_start(), .extract_response(),
.build_simulated_response(), and
.compute_endpoints(). The midpoint interval geometry
(type = "m") is now the only option and is hardcoded
internally. Users who previously relied on type = "l" or
type = "r" should use prepare_data() to supply
custom left/right endpoints instead.
Renamed bs_prepare() to
prepare_data(): The data preparation function has
been renamed to prepare_data() to be more descriptive and
consistent with the package’s verb-based API. The returned data frame
now carries the is_prepared attribute instead of
bs_prepared.
delta argument in simulation
functions: betaregscale_simulate() and
betaregscale_simulate_z() gain a delta
argument (default NULL) that forces all simulated
observations to a specific censoring type: 0 (exact), 1 (left), 2
(right), or 3 (interval). This enables targeted Monte Carlo studies
where the analyst controls the censoring structure.
When delta is non-NULL, the actual simulated values
(y_raw = rbeta(n, a, b)) are preserved on the scale grid,
and the forced censoring indicator is passed to
check_response() as a vector. This ensures that each
observation retains its covariate-driven variation with
observation-specific endpoints.
The returned data frame carries
attr(, "bs_prepared") = TRUE so that
betaregscale(), betaregscale_loglik(), and all
fitting functions use the pre-computed left,
right, yt, and delta columns
directly, bypassing the automatic boundary classification. Without this
attribute, the fitting pipeline would re-classify the response from the
y column alone, which would ignore the forced
delta.
delta argument in
check_response(): accepts an integer vector of
pre-specified censoring indicators, overriding the automatic
boundary-based classification on a per-observation basis. The endpoint
formulas adapt to non-boundary observations:
| delta | condition | left (l_i) | right (u_i) |
|---|---|---|---|
| 0 | any | y / K | y / K |
| 1 | y = 0 | eps | lim / K |
| 1 | y != 0 | eps | (y + lim) / K |
| 2 | y = K | (K - lim) / K | 1 - eps |
| 2 | y != K | (y - lim) / K | 1 - eps |
| 3 | type “m” | (y - lim) / K | (y + lim) / K |
The distinction between boundary and non-boundary observations is essential: when delta = 1 is forced on a non-zero y, the upper bound uses the actual y value ((y + lim)/K) rather than the fixed boundary formula (lim/K). This preserves the information content of each observation.
Observation-specific endpoints in
bs_prepare(): the internal
.compute_endpoints() helper now uses the same adaptive
formulas as check_response() for analyst-forced left/right
censoring on non-boundary scores. Previously, delta = 1 always produced
right = lim/K and delta = 2 always produced
left = (K - lim)/K, regardless of the actual y
value.
Simulation with forced delta = 1 or
delta = 2: the internal
.build_simulated_response() helper previously replaced all
y values with boundary values (y_grid = rep(0, n) for delta
= 1, y_grid = rep(ncuts, n) for delta = 2). This produced
degenerate data where every observation had identical endpoints (e.g.,
all left = 0.995, right = 0.99999 for delta = 2),
destroying all covariate-driven variation and making regression fitting
impossible.
The fix preserves the actual simulated grid values
(y_grid = round(y_raw * ncuts)) and passes a forced delta
vector to check_response(), which computes
observation-specific endpoints using the actual y values.
Missing "bs_prepared" attribute on
simulation output: when delta was forced, the
simulation functions did not mark the output with
attr(, "bs_prepared") = TRUE. As a result,
betaregscale() would re-classify the response via
check_response(), silently overwriting the forced delta
with automatic boundary rules. The attribute is now set
correctly.
type parameter ("m", "l",
"r") is deprecated across all functions:
betaregscale(), betaregscale_fit(),
betaregscale_fit_z(), betaregscale_loglik(),
betaregscale_loglik_z(),
betaregscale_simulate(),
betaregscale_simulate_z(), check_response(),
and prepare_data(). Use prepare_data() to
control interval geometry instead. The parameter still works but emits a
deprecation warning when passed explicitly.bs_prepare() data preprocessing: new
analyst-facing function that validates, classifies censoring, and
rescales raw data before model fitting. Supports four flexible input
modes: score-only, score + explicit delta, interval endpoints with NA
patterns, and analyst-supplied left/right bounds. Prepared data is
automatically detected by betaregscale()..extract_response() enables transparent
detection of bs_prepare()-processed data across all
fitting, log-likelihood, and starting-value functions.censoring_summary() now also accepts data frames from
bs_prepare().bs_prepare()
receives a subset data frame with non-sequential row names. Output now
always has sequential row names (1:n).bbmle. All model fitting now uses
stats::optim() directly with analytical gradients via the
C++ backend.betaregscale_bbmle() function has been
removed.cumulative parameter has been replaced by the
delta indicator vector, which supports mixed censoring
types within the same dataset.dados renamed to data across all
functions.betaregscale_simula_dados() is now
betaregscale_simulate(), and
betaregscale_simula_dados_z() is now
betaregscale_simulate_z().coef() and
vcov() now accept
model = c("full", "mean", "precision") argument.nobs(), formula(),
model.matrix(), confint(), and
plot().confint() provides Wald confidence intervals based on
the asymptotic normal approximation (z-test, not t-test).plot() method with six diagnostic panels (residuals vs
indices, Cook’s distance, residuals vs linear predictor, residuals vs
fitted, half-normal envelope, predicted vs observed) and both base R and
ggplot2 backends.censoring_summary() function for visual and tabular
summaries of the censoring structure, with both base R and ggplot2
backends.predict() expanded with five types:
"response", "link", "precision",
"variance", and "quantile". Supports
newdata for both fixed and variable dispersion models.residuals() supports five types:
"response", "pearson", "rqr"
(randomized quantile residuals), "weighted", and
"sweighted".summary() output now shows separate coefficient tables
for mean and precision submodels with Wald z-tests.predict() with newdata for
variable-dispersion models.pnorm() (standard normal) instead
of pt() (Student-t), consistent with Wald inference theory
(Eq. 2.34–2.35).bbmle-based fitting.coef, vcov,
fitted, residuals, summary,
print.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.