Package index • betaregscale

Model Fitting

Core functions for fitting beta interval regression models.

brs(): Fit a beta interval regression model
brs_fit_fixed(): Fit a fixed-dispersion beta interval regression model
brs_fit_var(): Fit a variable-dispersion beta interval regression model
brs_coef(): Internal coefficient table (deprecated, use brs_est() or summary())

Simulation

Data simulation for Monte Carlo studies.

brs_sim(): Simulate data from beta interval models

S3 Methods

Standard methods for fitted model objects of class brs.

coef(<brs>): Extract model coefficients
vcov(<brs>): Variance-covariance matrix of estimated coefficients
summary(<brs>): Summarize a fitted model (betareg style)
print(<brs>): Print a fitted model (brief betareg style)
print(<summary.brs>): Print a model summary (betareg style)
logLik(<brs>): Extract log-likelihood
AIC(<brs>): Akaike information criterion
BIC(<brs>): Bayesian information criterion
nobs(<brs>): Number of observations
formula(<brs>): Extract model formula
model.matrix(<brs>): Extract design matrix
fitted(<brs>): Extract fitted values
residuals(<brs>): Extract residuals
predict(<brs>): Predict from a fitted model
confint(<brs>): Wald confidence intervals
plot(<brs>): Diagnostic plots for beta interval regression

Diagnostics and Summaries

Functions for model diagnostics and censoring summaries.

brs_cens(): Graphical and tabular censoring summary
brs_est(): Coefficient estimates with inference
brs_gof(): Goodness-of-fit measures
brs_hessian(): Extract the Hessian matrix
autoplot.brs(): ggplot2 autoplot for brs models

Analyst Tools

Post-estimation tables, effects, score probabilities, and validation.

brs_table(): Compare fitted brs models in a single table
brs_marginaleffects(): Marginal effects for brs models
brs_predict_scoreprob(): Predict score probabilities from a fitted brs model
brs_cv(): K-fold cross-validation for brs models

Data Preparation

Functions for preparing response data and beta reparameterization.

brs_prep(): Pre-process analyst data for beta interval regression
brs_check(): Transform and validate a scale-derived response variable
brs_repar(): Reparameterize (mu, phi) into beta shape parameters