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()) are available
    for fitted objects.

Generates robust confidence intervals for standardized regression coefficients
    using heteroskedasticity-consistent standard errors for models fitted by lm()
    as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>.
    The package can also be used to generate confidence intervals for R-squared,
    adjusted R-squared, and differences of standardized regression coefficients.
    A description of the package and code examples
    are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

It computes betas-select, coefficients after standardization in
  structural equation models and regression models, standardizing only selected
  variables. Supports models with moderation, with product terms formed after
  standardization. It also offers confidence intervals that account for
  standardization, including bootstrap confidence intervals as proposed by
  Cheung et al. (2022) <doi:10.1037/hea0001188>. An introduction to the package
  can be found in Sun et al. (2026) <doi:10.1080/00273171.2026.2672692>.

Quantify the stability of each community based on the beta 
    diversity between communities gathered in a single sampling event rather 
    than a series of continuous sampling activities.