Provides functions for designing phase II clinical trials adjusting for the heterogeneity of the population using known subgroups or historical controls.

Functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.

User-friendly functions for leveraging (multiple) historical data set(s)
    for generalized linear models. Contains functions for sampling from the posterior
    distribution of a generalized linear model using the prior induced by the Bayesian 
    hierarchical model, power prior by Ibrahim and Chen (2000) <doi:10.1214/ss/1009212673>, 
    normalized power prior by Duan et al. (2006) <doi:10.1002/env.752>, normalized
    asymptotic power prior by Ibrahim et al. (2015) <doi:10.1002/sim.6728>, commensurate
    prior by Hobbs et al. (2011) <doi:10.1111/j.1541-0420.2011.01564.x>, robust 
    meta-analytic-predictive prior by Schmidli et al. (2014) <doi:10.1111/biom.12242>,
    and the latent exchangeability prior (LEAP) by Alt et al. (2023) <arXiv:2303.05223>.
    The package compiles all the 'CmdStan' models once during installation using the
    'instantiate' package.

Binary segmentation methods for detecting and estimating multiple change-points in the mean or second-order structure of high-dimensional time series as described in Cho and Fryzlewicz (2014) <doi:10.1111/rssb.12079> and Cho (2016) <doi:10.1214/16-EJS1155>.

Perform mediation analysis in the presence of high-dimensional
    mediators based on the potential outcome framework. High dimensional
    Bayesian mediation (HDBM), developed by Song et al (2018)
    <doi:10.1101/467399>, relies on two Bayesian sparse linear mixed models to
    simultaneously analyze a relatively large number of mediators for a
    continuous exposure and outcome assuming a small number of mediators are
    truly active. This sparsity assumption also allows the extension of
    univariate mediator analysis by casting the identification of active
    mediators as a variable selection problem and applying Bayesian methods
    with continuous shrinkage priors on the effects.