Package: betaregscale 3.1.1

José Evandeilton Lopes

betaregscale: Beta Regression for Interval-Censored Scale-Derived Outcomes

Maximum-likelihood estimation of beta regression models for responses derived from bounded rating scales. Observations are treated as interval-censored on (0, 1) after a scale-to-unit transformation, and the likelihood is built from the difference of the beta CDF at the interval endpoints. The complete likelihood supports mixed censoring types: uncensored, left-censored, right-censored, and interval-censored observations. Both fixed- and variable-dispersion submodels are supported, with flexible link functions for the mean and precision components. A compiled C++ backend (via 'Rcpp' and 'RcppArmadillo') provides numerically stable, high-performance log-likelihood evaluation. Standard S3 methods (print(), summary(), coef(), fitted(), residuals(), predict(), plot(), confint(), vcov(), logLik(), AIC(), BIC(), simulate()) are available for fitted objects.

Authors:José Evandeilton Lopes [aut, cre], Wagner Hugo Bonat [aut]

betaregscale_3.1.1.tar.gz
betaregscale_3.1.1.zip(r-4.7-x86_64)betaregscale_3.1.1.zip(r-4.7-arm64)betaregscale_3.1.1.zip(r-4.6-x86_64)betaregscale_3.1.1.zip(r-4.6-arm64)
betaregscale_3.1.1.tar.gz(r-4.7-arm64)betaregscale_3.1.1.tar.gz(r-4.7-x86_64)betaregscale_3.1.1.tar.gz(r-4.6-arm64)betaregscale_3.1.1.tar.gz(r-4.6-x86_64)
betaregscale_3.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
betaregscale/json (API)

# Install 'betaregscale' in R:
install.packages('betaregscale', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/evandeilton/betaregscale/issues

Pkgdown/docs site:https://evandeilton.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

3.86 score 12 scripts 340 downloads 28 exports 21 dependencies

Last updated from:e5a4c315da. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK251
linux-devel-x86_64OK298
source / vignettesOK257
linux-release-arm64OK258
linux-release-x86_64OK292
windows-devel-arm64OK414
windows-devel-x86_64OK387
windows-release-arm64OK382
windows-release-x86_64OK418
wasm-releaseOK106

Exports:autoplot.brsautoplot.brs_bootstrapautoplot.brs_marginaleffectsautoplot.brsmmbrsbrs_bootstrapbrs_censbrs_checkbrs_coefbrs_cvbrs_estbrs_fit_fixedbrs_fit_varbrs_gofbrs_hessianbrs_loglik_contribbrs_marginalbrs_marginaleffectsbrs_predict_scoreprobbrs_prepbrs_reparbrs_simbrs_tablebrsmmbrsmm_cvbrsmm_re_studybrsmm_simranef

Dependencies:clicpp11farverFormulaggplot2gluegtableisobandlabelinglifecyclenumDerivR6RColorBrewerRcppRcppArmadillorlangS7scalesvctrsviridisLitewithr

Advanced workflows: dispersion, cutpoints and model comparison
Variable dispersion | Boundary cutpoints | Model comparison | Links and interval maps | Comparison with an ordinal model | A Monte Carlo study | References

Last update: 2026-10-22
Started: 2026-02-25

Analyst tools: predictions, residuals and validation
Data and fit | Predictions | Score-scale quantities with standard errors | Average marginal effects | Residuals | Plots | Parametric bootstrap | Cross-validation | Tables | References

Last update: 2026-10-22
Started: 2026-02-25

Introduction to betaregscale
The score scale | The latent beta model | Interval maps and censoring types | The likelihood is a pmf over the scores | Estimation | Preparing data with brs_prep() | Choosing the interval map | Boundary cutpoints | References

Last update: 2026-10-22
Started: 2026-02-25

Mixed models with brsmm
The model | Integration methods | Simulated data | Fitting | Random effects | Testing variance components | Conditional and marginal predictions | Residuals | Group-level cross-validation | Simulation from a fit | Boundary cutpoints | References

Last update: 2026-10-22
Started: 2026-02-25

Readme and manuals

Help Manual

Help pageTopics
Akaike information criterionAIC.brs
AIC for brsmm modelsAIC.brsmm
Likelihood-ratio comparison of nested beta interval modelsanova.brs
Likelihood-ratio comparison involving mixed modelsanova.brsmm
ggplot2 autoplot for brs modelsautoplot.brs
ggplot2 autoplot for bootstrap resultsautoplot.brs_bootstrap
ggplot2 autoplot for marginal effectsautoplot.brs_marginaleffects
ggplot2 autoplot for brsmm modelsautoplot.brsmm
Bayesian information criterionBIC.brs
BIC for brsmm modelsBIC.brsmm
Fit a beta interval regression modelbrs
Parametric bootstrap confidence intervals for brs modelsbrs_bootstrap print.brs_bootstrap
Graphical and tabular censoring summarybrs_cens
Map scale scores to intervals on (0, 1) and censoring typesbrs_check
Internal coefficient table (deprecated, use brs_est() or summary())brs_coef
K-fold cross-validation for brs modelsbrs_cv
Coefficient estimates with inferencebrs_est
Goodness-of-fit measuresbrs_gof
Extract the Hessian matrixbrs_hessian
Log-likelihood contributions per observation or per groupbrs_loglik_contrib
Population-averaged score probabilities, expected scores and exceedancebrs_marginal
Marginal effects for brs modelsbrs_marginaleffects
Predict score probabilities from a fitted brs modelbrs_predict_scoreprob
Pre-process analyst data for beta interval regressionbrs_prep
Beta shape parameters from the parameters of each schemebrs_repar
Simulate data from beta interval modelsbrs_sim
Compare fitted brs models in a single tablebrs_table
Fit a mixed-effects beta interval regression modelbrsmm
Group-level cross-validation for brsmm modelsbrsmm_cv
Random-effects study for brsmm modelsbrsmm_re_study
Simulate clustered scores from a mixed beta interval modelbrsmm_sim
Extract model coefficientscoef.brs
Extract coefficients from a brsmm fitcoef.brsmm
Wald confidence intervalsconfint.brs
Wald confidence intervals for brsmm modelsconfint.brsmm
Extract fitted valuesfitted.brs
Fitted values from a brsmm modelfitted.brsmm
Extract model formulaformula.brs
Extract model formulaformula.brsmm
Extract log-likelihoodlogLik.brs
Log-likelihood for brsmm modelslogLik.brsmm
Extract design matrixmodel.matrix.brs
Extract design matrixmodel.matrix.brsmm
Number of observationsnobs.brs
Number of observations in a brsmm fitnobs.brsmm
Diagnostic plots for beta interval regressionplot.brs
Diagnostic plots for mixed beta interval regressionplot.brsmm
Predict from a fitted modelpredict.brs
Predict from a brsmm modelpredict.brsmm
Print a fitted model (brief betareg style)print.brs
Print a fitted brsmm modelprint.brsmm
Print a random-effects studyprint.brsmm_re_study
Print a model summary (betareg style)print.summary.brs
Print summary for brsmm modelsprint.summary.brsmm
Extract random effectsranef
Extract random effects from a brsmm modelranef.brsmm
Residuals of a fitted beta interval modelresiduals.brs
Residuals from a brsmm modelresiduals.brsmm
Simulate responses from a fitted beta interval modelsimulate.brs simulate.brsmm
Summarize a fitted beta interval modelsummary.brs
Summarize a fitted brsmm modelsummary.brsmm
Variance-covariance matrix of estimated coefficientsvcov.brs
Variance-covariance matrix for brsmm coefficientsvcov.brsmm