Fits latent threshold model for simulated data
    and describes how to adjust model using real data. Implements algorithm
    proposed by Nakajima and West (2013) <doi:10.1080/07350015.2012.747847>. 
    This package has a function to generate data, a function to configure 
    priors and a function to fit the model. Examples may be checked inside 
    the demonstration files.

Facilitates the importation of the Boston Blue Bike trip data
    since 2015. Functions include the computation of trip distances of
    given trip data. It can also map the location of stations within a
    given radius and calculate the distance to nearby stations. Data is
    from <https://www.bluebikes.com/system-data>.

Computes Blyth-Still-Casella exact binomial confidence intervals based on a refining procedure proposed by George Casella (1986) <doi:10.2307/3314658>.

Package for Bayesian model averaging and variable selection for linear models,
        generalized linear models and survival models (cox
        regression).

An implementation of the Bayesian model averaging method 
    of Psioda and others (2019) <doi:10.1093/biostatistics/kxz014> for basket trials. 
    Contains a user-friendly wrapper for simulating basket trials under conditions 
    and analyzing them with a Bayesian model averaging approach.

Toolkit for Bayesian estimation of the dependence structure in multivariate extreme value parametric models, following Sabourin and Naveau (2014) <doi:10.1016/j.csda.2013.04.021> and Sabourin, Naveau and Fougeres (2013) <doi:10.1007/s10687-012-0163-0>.

Multivariate tool for analyzing genome-wide association
    study results in the form of univariate summary statistics. The 
    goal of 'bmass' is to comprehensively test all possible multivariate
    models given the phenotypes and datasets provided. Multivariate
    models are determined by assigning each phenotype to being either
    Unassociated (U), Directly associated (D) or Indirectly associated
    (I) with the genetic variant of interest. Test results for each model 
    are presented in the form of Bayes factors, thereby allowing direct
    comparisons between models. The underlying framework implemented
    here is based on the modeling developed in "A Unified Framework 
    for Association Analysis with Multiple Related Phenotypes",
    M. Stephens (2013) <doi:10.1371/journal.pone.0065245>.