Fit beta calibration models and obtain calibrated probabilities from
    them.

A family of novel beta mixture models (BMMs) has been developed by Majumdar et al. (2022) <doi:10.48550/arXiv.2211.01938> to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility.

Generates confidence intervals for standardized regression coefficients
    using delta method standard errors for models fitted by lm()
    as described in Yuan and Chan (2011) <doi:10.1007/s11336-011-9224-6>
    and Jones and Waller (2015) <doi:10.1007/s11336-013-9380-y>.
    The package can also be used to generate confidence intervals for
    differences of standardized regression coefficients 
    and as a general approach to performing the delta method.
    A description of the package and code examples
    are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

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>.