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>.

The four functions svdcp() ('cp' for column partitioned), svdbip() or svdbip2() ('bip' for bipartitioned), and svdbips() ('s' for a simultaneous optimization of a set of 'r' solutions), correspond to a singular value decomposition (SVD) by blocks notion, by supposing each block depending on relative subspaces, rather than on two whole spaces as usual SVD does. The other functions, based on this notion, are relative to two column partitioned data matrices x and y defining two sets of subsets x_i and y_j of variables and amount to estimate a link between x_i and y_j for the pair (x_i, y_j) relatively to the links associated to all the other pairs. These methods were first presented in: Lafosse R. & Hanafi M.,(1997) <https://eudml.org/doc/106424> and Hanafi M. & Lafosse, R. (2001) <https://eudml.org/doc/106494>.