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Changelog

Source: NEWS.md

betaregscale 3.0.0

Major release: some calls accepted by 2.7.4 now stop and some results change. With the defaults (repar = 2, interval = "mid", lim = 0.5) and well-behaved data, estimates agree with 2.7.4 to optimiser tolerance; the sections below give every change and its reason.

Breaking changes

  • Links are checked against repar: identity, inverse and 1/mu^2 are rejected for positive parameters, and under repar = 1 the default link_phi is now "log" (it was "logit", which capped the precision at 1).
  • Fits change on data with observations far in a tail (the 1e-15 probability floor of the likelihood is gone), and new starting values move some variable-dispersion fits to higher optima.
  • Input accepted with a warning or silently now stops: lim outside (0, 0.5], scores outside 0..ncuts, delta = 3 with left == right, NA in a prepared delta, rank-deficient designs.
  • anova() refuses fits with different interval, ncuts, lim or nobs.
  • summary() of a brsmm fit reports SD/Corr intervals instead of z-tests on log(sd); the ICC of brsmm_re_study() uses the beta level-1 variance instead of pi^2/3.
  • vcov() returns NA (it returned 0) for variances it cannot estimate. The default Hessian is the compiled one (hessian_method = "cpp"; standard errors agree with numDeriv to 1e-8).
  • brs_check() treats values in (0, 1) as exact per observation, as brs_prep() does.
  • QMC with two or more random effects is a different estimator (symmetric-root scaling of the nodes).

Bootstrap, fit diagnostics and mixed-model inference

  • brs_bootstrap() simulates only the response, at the fitted shapes, and refits the original formula on a copy of the data: factors, log(x), 0 + x, any response name and variable dispersion work (they ended in “Too few successful bootstrap replicates (0)”). Each row keeps its observation mechanism: exact values stay continuous (they were re-gridded to delta = 3), scores are re-coarsened on the fit’s grid, analyst thresholds (brs_prep() Modes 2-4) are kept as fixed, non-informative thresholds and delta is re-drawn by the cell of their partition where the new value falls (conservative when the original design had more thresholds than a row records; an approximation for Mode 2 rows with a forced delta). Failed replicates are counted (n_failed, fail_rate) and printed; the “basic” MCSE limits were swapped; "bca" warns once per session that it is an approximation.
  • brs()/brsmm() stop on rank-deficient design matrices, warn on near collinearity, and check the gradient, the Hessian and the likelihood clamps after optim() (fit$diagnostics, one-line warnings; the gradient check is the log-likelihood gain of the remaining Newton step, above 0.01). vcov() no longer uses a generalised inverse: variances it cannot estimate are NA (they were 0). brsmm() flags a variance component on the boundary. logLik() of a brs() fit is evaluated exactly at the returned estimate.
  • brsmm: summary() reports SD/Corr with transformed Wald intervals and no z-test on log(sd); anova() uses the chi-bar-square mixture 1/2 chi2(Df - 1) + 1/2 chi2(Df) when a model adds one random-effect term (1/2 chi2(0) + 1/2 chi2(1) for brs against a random intercept) and says so in its heading; the ICC of brsmm_re_study() uses the beta level-1 variance of logit(Y) instead of pi^2/3 (NA with a warning when the clamp of the mean drives it, e.g. probit or cloglog links with a large random-effect SD); control is merged into list(maxit = 2000).
  • summary() leaves the RNG state untouched. brs_check() treats values in (0, 1) as exact per observation, as brs_prep() does, and warns on input mixing them with values >= 1 (half-point scores: use y * 2 and ncuts * 2). brs_sim(delta = 1 or 2) warns (informative censoring, no finite MLE) and keeps covariates of 0 + x formulas; brs_prep() warns on rows covering the whole scale. Marginal effects honour | 0 + z; repar 2 variable-dispersion starts use the moment intercept with zero slopes. This changes some fits, to an equal or higher log-likelihood with fewer iterations, and a brsmm variable-dispersion fit (200 groups x 25) that had “converged” to a wrong point (intercept 0.94, true 0.2) now gains 1214 in log-likelihood (intercept 0.14).

Interval direction (interval)

  • New interval = c("mid", "right", "left") in brs_check(), brs_prep(), brs(), brsmm() and brs_sim() (the dissertation’s m, r, l). Score s maps to [s - lim, s + lim] / K ("mid", default, unchanged) or to [s, s + 1] / (K + 1) ("right"/"left": K + 1 equal cells). This is a package choice that differs from the dissertation (which divides by K): "right" and "left" give the same likelihood and differ only in predict(type = "score"), so chapter 4’s opposite intercept biases vanish. delta comes from the score (0 -> 1, K -> 2, else 3).

  • brs_sim() generates scores by the same mechanism the likelihood assumes and always attaches is_prepared/ncuts/lim/interval; brs() and brsmm() reuse these attributes.

  • lim outside (0, 0.5] is an error, lim < 0.5 warns (partial coarsening). anova() refuses fits with different interval, ncuts, lim (only under "mid") or nobs. New predict() types "score" (latent score) and "expected_score" (expected recorded score).

  • brs_check() now stops on scores outside 0..K (it warned and the observation contributed -1e6 to the log-likelihood), and brs_check()/ brs_prep() reject delta = 3 with left == right. brs_prep() checks analyst bounds against the latent range of the direction and makes an analyst interval that reaches 0 or 1 left- or right-censored.

  • The three schemes of brs_repar() model different parameters, so the admissible links now depend on repar and incompatible combinations are rejected with an error that prints the table: repar = 0 (shapes p, q, both on (0, Inf)): link and link_phi in {log, sqrt}; repar = 1 (mean, precision): link in {logit, probit, cauchit, cloglog}, link_phi in {log, sqrt}; repar = 2 (mean, dispersion): both in {logit, probit, cauchit, cloglog}. identity, inverse and 1/mu^2 are no longer accepted for positive parameters (their inverse does not map the real line onto (0, Inf)); with sqrt a warning is issued after the fit when a fitted linear predictor is <= 0 (flat inverse link). Link names are matched exactly (no partial matching).

  • link and link_phi default to NULL in brs(), brs_fit_fixed(), brs_fit_var(), brsmm() and brs_sim(), and resolve by repar: 0 -> log/log, 1 -> logit/log, 2 -> logit/logit. The old default link_phi = "logit" under repar = 1 squashed the precision into (0, 1): with true precision 20 it estimated 0.999999 (log-likelihood -2483 against -1885 with the log link).

  • Under repar = 0 the fitted object keeps the shape p in hatmu / fitted_mu (and brs_repar(mu = ) takes p > 0), but every user-facing mean now is E[Y] = a / (a + b): fitted(), predict(type = "response"), response/Pearson residuals, marginal effects and the calibration plots. predict() used to return p (0.949 where the mean was 0.493). Weighted/sweighted residuals are computed from the shapes (digamma(a) - digamma(b), trigamma(a) + trigamma(b)), the pseudo R-squared compares E[Y] and y on the logit scale under repar = 0, and starting values for the shapes come from the method of moments. brs_predict_scoreprob(newdata = ), brs_cv() and the CDF plot no longer feed predict(type = "response") back into brs_repar(). Identical results under repar = 1, 2.

  • brsmm() now runs the same input validation as brs(): repar outside 0:2 (which used to fall into the C++ default: branch silently), ncuts, lim and the links.

  • ncuts and lim default to NULL in brs() and brsmm() and are taken from the attributes that brs_prep() (and now brs_sim()) store on the data; an explicit value that differs from the stored one is ignored with a warning. Refitting prepared data with another ncuts made brs_predict_scoreprob() rows sum to 0.05 instead of 1. brs_sim() always attaches is_prepared, ncuts and lim (it did so only when delta was given).

  • The compiled likelihood clamped the inverse-linked first parameter to (1e-5, 1 - 1e-5) for every repar, so under repar = 0 the truth and the “MLE” were both evaluated with the shape p capped at 0.99999. clamp_mu_by_repar() in src/brs_common.h now uses [1e-5, 1e8] for the shape and [1e-5, 1 - 1e-5] for the mean, and the R side (hatmu, hatphi, predict()) applies the same clamps.

Row alignment and input validation

  • .extract_response() and .brsmm_row_index() treated numeric row names as row positions. After data[-10, ], a permutation, or any subset that keeps the original row names, left/right/delta and the grouping variable were taken from the wrong rows, silently corrupting brs() on subsetted data, brs_cv(), the BCa jackknife in brs_bootstrap() and brsmm(). Rows are now always mapped by match(rownames(mf), rownames(data)).

  • The compiled brsmm() likelihood did no dimension checks. With a NA in a random-slope variable, model.matrix() dropped a row and the group builder wrote past its buffers (AddressSanitizer heap-buffer-overflow). Vector lengths, group >= 1 and delta in 0:3 are now validated in C++, and a NA in a random-effects variable gives a clear R-side error.

  • brs_prep() Mode 3 rows (only left/right known) had y = NA, so model.frame() dropped them and those censored observations never reached the fit. They are now kept.

Likelihood: no probability floor, exact tails

  • The compiled likelihood floored every censored probability at 1e-15 before taking the log, and chose the CDF tail by the position of the interval on (0, 1) rather than by the fitted distribution. An interval far above a small fitted mean (or below a large one) was computed as F(right) - F(left) with both terms equal to 1 to machine precision, so it hit the floor: the observation contributed the constant log(1e-15) with zero gradient, and the optimiser maximised a trimmed likelihood that ignored outliers. Now the tail is chosen by the mean a / (a + b), pbeta() is evaluated in plain scale on the small side, there is no floor, and below 1e-240 (where R’s bratio loses accuracy) an endpoint Laplace approximation of the tail integral takes over. The endpoint clamp to [1e-5, 1 - 1e-5] is unchanged. An R mirror of the same rules, .brs_obs_loglik(), is used by brs_cv() for the log-score.

    User-visible consequences: logLik(), AIC() and brs_cv() change on data with observations far in a tail, and the precision estimate can drop a lot. In an example with 200 observations and 4 outliers the estimated precision went from 328 to 32; the old value was an artefact of the trimmed likelihood. An interval-censored observation with left == right (probability zero) now contributes -1e6 instead of log(1e-15).

Compiled backend: Armadillo, chain-rule derivatives, stable standard errors

  • The mixed-model backend (brsmm()) is now written in RcppArmadillo, like the rest of the package; RcppEigen is no longer a dependency.
  • Gradients and Hessians use the chain rule on the linear predictors (per-observation central differences, cost independent of the number of coefficients). The new default hessian_method = "cpp" is about 16x faster than numDeriv and agrees with it to 1e-8 in the standard errors (hessian_method = "numDeriv" remains); brs() fits are 2.0-2.3x faster.
  • brs() accepts start and control. Bootstrap and jackknife refits warm-start from the parent estimate and use the compiled Hessian: 1.4-1.8x faster for R = 100, n = 250, with intervals unchanged to 6e-5 standard errors.
  • brs_marginaleffects() draws from the Cholesky factor of the variance matrix instead of its eigenvectors, so a negligible change of the variance matrix no longer changes the simulated standard errors (an eigenvector sign flip moved them by about 2% with n_sim = 400).
  • brsmm() passes the gradient of the chosen approximation (Laplace, AGHQ or QMC; chain rule and implicit-function theorem at the modes) to optim() and computes the Hessian from it (hessian_method = "cpp", default). Standard errors of random-slope models are now finite and reproducible (they were NaN or changed by 20-40% between two practically identical optima). Fits are 1.5-6.6x faster.
  • The inner search for the random-effect modes is a Levenberg–Marquardt Newton method with warm starts. It no longer returns b = 0 when the curvature there is indefinite, and the silent eigenvalue floor of 1e-8 (which added up to +9.2 per direction to the Laplace value) is gone; a group without a positive-definite mode is penalised and reported in fit$diagnostics$inner.
  • AGHQ and QMC scale the nodes by the symmetric root C^(-1/2) of the curvature, so their values no longer depend on the eigenvector sign and order conventions of the LAPACK in use (random-effect dimension >= 2). For two or more random effects QMC is therefore a different estimator than before; at 1024 points it underestimates the log-likelihood (mean error -0.05 over 30 two-effect data sets, -0.03 with the old scaling), so int_method = "aghq" is recommended up to three random effects. Warm starts of the inner modes are reset at every brsmm() call, so a fit depends only on its data and start.
  • Structural errors in the compiled functions (wrong parameter length, NA or non-finite data, group codes beyond the number of rows) stop with a clear message; a NaN parameter gives the likelihood penalty, +-Inf the bounds. An NA in a prepared delta column now stops in R and in C++; before, it reached the compiled code as NaN and was cast to an integer (undefined behaviour: rejected with a misleading message on x86-64, silently read as an exact observation on arm64).

Documentation

  • Help pages aligned with Lopes (2023) and with the code: the complete likelihood with the four censoring types (the dissertation’s table swaps delta = 1 and 2; the package follows its equation), the three parameterisations with their links, mean and variance (repar = 2 dispersion is 1 / (1 + a + b), not a coefficient of variation), the interval directions, the border handling that replaces the dissertation’s edge transformation, the residual types, estimation (chain-rule gradient and Hessian) and a “Fit diagnostics” section explaining each warning.
  • ncuts is documented as K, the maximum score (scale 0..K, K + 1 categories); a scale that starts at 1 (Likert 1-5) is shifted to 0-4.
  • New help pages for summary(), confint() and anova() (Wald, LRT and the chi-bar-square mixture); brsmm() documents the integration methods, the gradient and the boundary diagnostics.
  • New examples, runnable without \donttest: a synthetic NRS-11 study (times x groups), the four brs_prep() modes and a Likert item, one simulation per parameterisation, random intercept and slope with anova(), bootstrap with a factor, cross-validation, marginal effects.
  • Vignettes rewritten where they disagreed with the code: likelihood, directions, parameterisations, diagnostics, residuals (brs-intro); a Monte Carlo study in the dissertation’s design (brs-advanced-workflows); ICC, SD/Corr intervals and the chi-bar-square test (brs-mm); marginal effects and score probabilities (brs-analyst-tools). The intro vignette uses a percentile bootstrap instead of BCa, which lowers the build time.
  • README: removed the claims of an analytical gradient, of new methodology (the parameterisation is Bayer, 2011) and of exactly normal quantile residuals; ncuts wording fixed; references added. The package page attributes the M1/M2/M3 comparison to the dissertation’s study.
  • brs_prep() accepts a bound or score column that is entirely NA (logical in R) as numeric NA instead of stopping.

betaregscale 2.7.4

CRAN release: 2026-08-23

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.

Vignette build time

  • 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.

Documentation

  • ?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.

betaregscale 2.7.3

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.

Numerical accuracy

  • The interval probability 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.

Bug fixes

  • Removed two stale help pages, 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.
  • Removed a Dropbox conflict copy of .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.

Packaging

  • 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.

Documentation

  • 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.
  • The package help page (?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.
  • The URI of the cited master’s dissertation is no longer wrapped in \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.

Internal

  • Removed the unused Armadillo mixed-effects backend from 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.

betaregscale 2.7.1

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.

Bug fixes

  • Fixed the inverse link for the precision (dispersion) submodel, which could return a negative shape near the origin; the dispersion parameter is now always positive.
  • Corrected deviance residuals to use the proper saturated-model log-likelihood (affecting 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.
  • The sqrt link for the precision submodel now clamps the linear predictor to be non-negative, preserving the correct gradient sign during optimization.
  • Mixed-effects mode finding (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.
  • The adaptive Gauss-Hermite (AGHQ) integration grid now uses 64-bit indexing to prevent an integer-overflow crash for four or more random-effect dimensions, and the quasi-Monte Carlo prime table was expanded (20 to 50 primes) to keep Halton sequences uncorrelated in higher dimensions.
  • Halton quantiles are clamped to (1e-9, 1 - 1e-9) to avoid infinite values at the boundary.

Improvements

  • The optimizer now emits a warning when it fails to converge in brs() and brsmm().
  • Pseudo-R2 reporting adds a caution note when more than 50% of observations are censored.
  • Link evaluation no longer constructs 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.
  • Starting values: 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).

Internal

  • Extracted a shared C++ header (src/brs_common.h) to remove duplicated code between the Armadillo and Eigen backends, and documented the numerical tolerance constants.
  • The Eigen backend reuses a single pre-allocated workspace vector when forming the numerical Hessian.
  • src/Makevars and src/Makevars.win no longer define -DARMA_NO_DEBUG, enabling Armadillo bounds checking.
  • The internal .brsmm_loglik_eigen entry point is exported with a leading dot to keep it out of the public namespace.

betaregscale 2.7.0

New features

  • 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.
    • For 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.

Improvements

  • Coefficient display: 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.
  • Calibration plots (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.

Testing

  • Added 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.

betaregscale 2.6.9

CRAN release: 2026-02-25

CRAN resubmission

Documentation and formatting fixes

  • Added missing \value, \seealso, and \examples{\donttest{...}} tags to multiple S3 method documentation files (print.summary, residuals, summary, vcov, ranef) to ensure full CRAN policy compliance.
  • Translated remaining Portuguese text into English in the mixed-effects vignette (vignettes/brs-mm.Rmd).
  • Corrected ranef() usage in vignettes to correctly call the generic function.
  • Fixed mathematical formulas rendering in README.md to be fully compatible with GitHub Markdown, and updated pkgdown site build configuration to load betaregscale appropriately during vignette setups.
  • Minor mathematical formatting and typographical fixes (e.g., en-dashes for page ranges) in README.md references.

betaregscale 2.6.8

New features

  • Completed S3 method standardization for brsmm (mixed-effects) objects to mirror the interface of brs (fixed-effects) objects:
    • Added missing extractors: formula(), model.matrix(), and confint().
    • Upgraded residuals() to support conditional "deviance", "rqr" (randomized quantile residuals), "weighted", and "sweighted" options.
    • Upgraded predict() to support conditional type = "quantile" evaluations directly.
    • Added ranef() generic and ranef.brsmm() method to extract random-effect modes.
  • Modified package helper functions brs_gof() and brs_est() to compute GOF properties and estimates directly from both brs and brsmm objects respectively.

Improvements

  • Standardized print.brsmm() to explicitly display mean, precision, and random-effect coefficient blocks side-by-side, mirroring the verbose visual style of print.brs().

Documentation

  • Complete @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()).
  • Removed all 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.
  • No \dontrun{} anywhere: all examples are either direct or wrapped in \donttest{} as appropriate.
  • Ferrari & Cribari-Neto (2004) DOI (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.
  • All autoplot.* examples updated to use ggplot2::autoplot() (explicit namespace) for reliability in check environments.

betaregscale 2.6.7

CRAN resubmission (Konstanze Lauseker review, 20 Feb 2026)

Bug fixes and CRAN policy compliance

betaregscale 2.6.6

CRAN resubmission (Uwe Ligges review, 18 Feb 2026)

betaregscale 2.6.5

New features

  • Extended brs_bootstrap() with ci_type = "bca" (bias-corrected and accelerated intervals), plus Monte Carlo diagnostics for interval endpoints (mcse_lower, mcse_upper).
  • Added Wald interval columns (wald_lower, wald_upper) to bootstrap output for direct asymptotic vs resampling comparison.
  • Added autoplot.brs_bootstrap() support to visually compare bootstrap and Wald intervals in type = "ci_forest".
  • Added autoplot.brs_marginaleffects() with three views: forest, magnitude, and dist.

Improvements

  • Improved robustness and efficiency in brs_marginaleffects():
    • central-difference AME approximation for numeric covariates,
    • scale-adaptive perturbation step,
    • one-time simulation draw generation reused across variables,
    • optional storage of AME draws via keep_draws = TRUE.
  • Refined brs_cens() output to include richer summary fields (percentage, severity, interpretation) and optional domain-agnostic interpretation messages via inform = TRUE.
  • Updated README and vignettes with examples for BCa bootstrap intervals, bootstrap visual diagnostics, and enhanced marginal-effects visualization workflow.

betaregscale 2.6.4

New features

  • Extended brsmm() to support multivariate random effects in the mean predictor, including random intercept + random slope specifications such as random = ~ 1 + x | group.
  • Added multivariate Laplace approximation in the Eigen C++ backend for group-specific latent vectors and covariance matrix handling via packed lower-Cholesky parameterization.
  • Added brsmm_group_modes_eigen() to compute posterior modes of group random effects for general random-effects dimension.
  • Added generic model-comparison methods anova.brs() and anova.brsmm() for likelihood-ratio workflow across brs and brsmm candidates.
  • Added brsmm_re_study() and print.brsmm_re_study() for numeric random-effects diagnostics (covariance/correlation, shrinkage, normality checks).

Improvements

  • Updated predict.brsmm(), vcov.brsmm(), and print.brsmm() to support both scalar (q_b=1) and vector (q_b>1) random-effects structures.
  • Expanded mixed-model test coverage with integration tests for random intercept + slope fits, covariance extraction (D), ranef, random-effects studies, and prediction behavior.
  • Updated README.md and vignettes with explicit multivariate mixed-model mathematics, Laplace formula in matrix form, and end-to-end model-selection examples.
  • Documentation references now use DOI-based validated links only (https://doi.org/...) to keep CRAN URL checks robust.

betaregscale 2.6.3

Improvements

  • Revised and expanded all core vignettes (brs-intro, brs-analyst-tools, brs-mm) with stronger mathematical exposition, explicit likelihood pieces by censoring type, and clearer inferential interpretation for analysts.
  • Updated vignettes and README to prioritize the package’s most important analyst-facing functions: brs_bootstrap(), brs_marginaleffects(), brs_predict_scoreprob(), brs_cv(), and brs_table().
  • Standardized vignette outputs with cleaner tabular presentation using knitr::kable(..., digits = 4) for better readability and reporting consistency.
  • Added and revised bibliographic references with validated DOI metadata and dual online source verification links in vignettes/README.
  • Re-rendered all vignettes and rebuilt documentation website (pkgdown) to keep articles and reference pages synchronized with the current API.

betaregscale 2.6.2

Improvements

  • Improved numerical stability in brsmm() by refining the optimization control and starting values.
  • Updated simulate() method to better handle edge cases in random effects simulation.
  • Enhanced methods.R for better compatibility with downstream packages.

betaregscale 2.6.1

Bug fixes

  • Renamed vignettes to avoid naming collisions with the package name, which caused pkgdown site build failures.
  • Updated _pkgdown.yml to reflect new vignette names.

betaregscale 2.6.0

New features

  • Added brsmm() for mixed-effects beta interval regression with Gaussian random intercepts (random = ~ 1 | group) using Laplace-approximated marginal likelihood.
  • Added C++ mixed-model likelihood core: .brsmm_loglik_laplace_cpp() and .brsmm_group_modes_cpp().
  • Added a first S3 interface for brsmm objects: print, summary, coef, vcov, logLik, AIC, BIC, nobs, fitted, predict, and residuals.

Improvements

  • Added test-brsmm.R with mixed-model fitting and prediction tests.
  • Corrected author name spelling in package metadata/documentation: José Evandeilton Lopes.

betaregscale 2.5.0

New features

  • Added brs_table() to compare one or more fitted brs models in a single table with logLik, AIC, BIC, pseudo-R2, and censoring composition.
  • Added brs_marginaleffects() for average marginal effects in the mean or precision submodel, with optional simulation-based uncertainty intervals.
  • Added autoplot.brs() with ggplot2 diagnostics for type = "calibration", type = "score_dist", type = "cdf", and type = "residuals_by_delta".
  • Added brs_predict_scoreprob() to obtain predicted probabilities on the original integer score scale.
  • Added brs_cv() for repeated k-fold cross-validation of brs models with fold-level predictive metrics (log_score, rmse_yt, and mae_yt).

Improvements

  • Updated package reference organization (pkgdown) to expose the new analyst-oriented tools.
  • Updated README.md and vignette content with examples for model comparison, marginal effects, and score-probability predictions.

betaregscale 2.4.0

Breaking changes

  • 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.

New features

  • brs_sim() is now the single simulation entry point for both fixed- and variable-dispersion models, with formula semantics aligned to brs().

Improvements

  • Release documentation was updated to reflect the consolidated simulation API and current exported function set.
  • brs_prep() consistency warnings are emitted once per call on final prepared output, improving test stability and warning capture behavior.

betaregscale 2.3.0

Breaking changes

  • API Overhaul: All exported functions have been renamed to use the compact brs_ prefix for consistency and ease of typing.
  • Class Renaming: The S3 class betaregscale has been renamed to brs. All associated S3 methods have been updated accordingly (e.g., summary.brs, plot.brs).

betaregscale 2.2.0

Breaking changes

  • 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.

betaregscale 2.1.1

New features

  • 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.

Bug fixes

  • 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.

Deprecations

  • The 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.

betaregscale 2.0.1

New features

  • 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().
  • Internal helper .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().
  • New vignette section documenting all four data preparation modes.

Bug fixes

  • Fixed potential row-indexing bug when bs_prepare() receives a subset data frame with non-sequential row names. Output now always has sequential row names (1:n).

betaregscale 2.0.0

Breaking changes

  • Removed dependency on bbmle. All model fitting now uses stats::optim() directly with analytical gradients via the C++ backend.
  • The betaregscale_bbmle() function has been removed.
  • The cumulative parameter has been replaced by the delta indicator vector, which supports mixed censoring types within the same dataset.
  • Parameter dados renamed to data across all functions.
  • Simulation functions renamed: betaregscale_simula_dados() is now betaregscale_simulate(), and betaregscale_simula_dados_z() is now betaregscale_simulate_z().

New features

  • Mixed censoring support: the complete likelihood (Eq. 2.24) now handles four censoring types simultaneously: exact (δ=0\delta=0), left-censored (δ=1\delta=1), right-censored (δ=2\delta=2), and interval-censored (δ=3\delta=3).
  • C++ backend rewrite: log-likelihood and analytical gradient functions rewritten in C++ (RcppArmadillo) for numerically stable, high-performance evaluation.
  • betareg-style S3 interface: coef() and vcov() now accept model = c("full", "mean", "precision") argument.
  • New S3 methods: 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.

Bug fixes

  • Fixed Pearson residual computation to correctly dispatch by reparameterization type (repar 1 vs repar 2).
  • Fixed predict() with newdata for variable-dispersion models.
  • Fixed p-values to use pnorm() (standard normal) instead of pt() (Student-t), consistent with Wald inference theory (Eq. 2.34–2.35).

betaregscale 1.1.1

  • Initial public release with bbmle-based fitting.
  • Support for fixed and variable dispersion models.
  • Basic S3 methods: coef, vcov, fitted, residuals, summary, print.