Package providing a number of functions for working with Two- and 
    Four-parameter Beta and closely related distributions (i.e., the Gamma-
    Binomial-, and Beta-Binomial distributions).
        Includes, among other things: 
    - d/p/q/r functions for Four-Parameter Beta distributions and Generalized
    "Binomial" (continuous) distributions, and d/p/r- functions for Beta-
    Binomial distributions.
    - d/p/q/r functions for Two- and Four-Parameter Beta distributions
    parameterized in terms of their means and variances rather than their
    shape-parameters.
    - Moment generating functions for Binomial distributions, Beta-Binomial 
    distributions, and observed value distributions.
    - Functions for estimating classification accuracy and consistency, 
    making use of the Classical Test-Theory based 'Livingston and Lewis' (L&L) 
    and 'Hanson and Brennan' approaches.
      A shiny app is available, providing a GUI for the L&L approach when used 
    for binary classifications. For url to the app, see documentation for the 
    LL.CA() function.
    Livingston and Lewis (1995) <doi:10.1111/j.1745-3984.1995.tb00462.x>.
    Lord (1965) <doi:10.1007/BF02289490>.
    Hanson (1991) <https://files.eric.ed.gov/fulltext/ED344945.pdf>.

Generates Monte Carlo confidence intervals
    for standardized regression coefficients (beta) and other effect sizes,
    including multiple correlation, semipartial correlations,
    improvement in R-squared, squared partial correlations,
    and differences in standardized regression coefficients,
    for models fitted by lm().
    'betaMC' combines ideas from Monte Carlo confidence intervals for the indirect effect
    (Pesigan and Cheung, 2023 <doi:10.3758/s13428-023-02114-4>)
    and the sampling covariance matrix of regression coefficients
    (Dudgeon, 2017 <doi:10.1007/s11336-017-9563-z>)
    to generate confidence intervals effect sizes in regression.

Generates nonparametric bootstrap confidence intervals
    (Efron & Tibshirani, 1993: <doi:10.1201/9780429246593>)
    for standardized regression coefficients (beta) and other effect sizes,
    including multiple correlation, semipartial correlations,
    improvement in R-squared, squared partial correlations,
    and differences in standardized regression coefficients,
    for models fitted by lm().