A Bayesian meta-analysis method for studying cross-phenotype
genetic associations. It uses summary-level data across multiple phenotypes to
simultaneously measure the evidence of aggregate-level pleiotropic association
and estimate an optimal subset of traits associated with the risk locus. CPBayes
is based on a spike and slab prior and is implemented by Markov chain Monte
Carlo technique Gibbs sampling.

A Bayesian meta-analysis method for studying cross-phenotype
genetic associations. It uses summary-level data across multiple phenotypes to
simultaneously measure the evidence of aggregate-level pleiotropic association
and estimate an optimal subset of traits associated with the risk locus. CPBayes
is based on a spike and slab prior.
MASS, stats, forestplot, grDevices, purrr, mvtnorm

A Bayesian meta-analysis method for studying cross-phenotype
genetic associations. It uses summary-level data across multiple phenotypes to
simultaneously measure the evidence of aggregate-level pleiotropic association
and estimate an optimal subset of traits associated with the risk locus. CPBayes
is based on a spike and slab prior. The methodology is available from: A Majumdar, T Haldar, S Bhattacharya, JS Witte (2018) <doi:10.1371/journal.pgen.1007139>.

Implements cluster-polarization coefficient for measuring distributional
polarization in single or multiple dimensions, as well as associated functions.
Contains support for hierarchical clustering, k-means, partitioning around medoids,
density-based spatial clustering with noise, and manually imposed cluster membership.

Concordance probability estimates in survival analysis
Qianxing Mo, Mithat Gonen and Glenn Heller
Functions to calculate concordance probability estimates in survival analysis

Concordance Probability Estimates in Survival Analysis

Functions to calculate concordance probability estimates
in survival analysis

Functions to calculate concordance probability estimates in survival analysis.

Anthony Christidis <anthony.christidis@stat.ubc.ca>,
Stefan Van Aelst <stefan.vanaelst@kuleuven.be>,
Ruben Zamar <ruben@stat.ubc.ca>
Functions to generate ensembles of generalized linear models using
competing proximal gradients. The optimal sparsity and diversity
tuning parameters are selected via an alternating grid search.