Perform mediation analysis in the presence of high-dimensional
    mediators based on the potential outcome framework. Bayesian Mediation
    Analysis (BAMA), developed by Song et al (2019) <doi:10.1111/biom.13189> and
    Song et al (2020) <arXiv:2009.11409>,
    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.

Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) <doi:10.18637/jss.v099.i11>.

Enables a user to consume the 'BambooHR' API endpoints using R. The
  actual URL of the API will depend on your company domain, and will be handled
  by the package automatically once you setup the config file. The API documentation
  can be found here <https://documentation.bamboohr.com/docs>.

Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).

Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework.
  The distribution parameters may capture location, scale, shape, etc. and every parameter may depend
  on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model.
  The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019)
  <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021)
  <doi:10.18637/jss.v100.i04>.