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\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 references.#
betaregscale 2.6.8brsmm
(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.
Health stats visible at Monitor.