Changelog • betaregscale

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

Added \value documentation to print.brs() and print.summary.brs() methods.
Replaced \dontrun{} with \donttest{} in brs_gof() example and created complete executable example.
Removed .GlobalEnv modification from brs_bootstrap() (CRAN policy violation).
Removed 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.
Removed seed parameter from all four functions listed above. Documentation updated with recommended usage pattern.

betaregscale 2.6.6

CRAN resubmission (Uwe Ligges review, 18 Feb 2026)

DESCRIPTION: function names in Title/Description now use parentheses (e.g. logLik(), coef()) per CRAN policy.
URL updated to GitHub repository (https://github.com/evandeilton/betaregscale) while pkgdown site is deployed.

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.
- 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()
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 ( $\delta=0$ ), left-censored ( $\delta=1$ ), right-censored ( $\delta=2$ ), and interval-censored ( $\delta=3$ ).
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.