cpgen: Parallelized Genomic Prediction and GWAS
Version 0.1

Frequently used methods in genomic applications with emphasis on parallel computing (OpenMP). At its core, the package has a Gibbs Sampler that allows running univariate linear mixed models that have both, sparse and dense design matrices. The parallel sampling method in case of dense design matrices (e.g. Genotypes) allows running Ridge Regression or BayesA for a very large number of individuals. The Gibbs Sampler is capable of running Single Step Genomic Prediction models. In addition, the package offers parallelized functions for common tasks like genome-wide association studies and cross validation in a memory efficient way.

Package details

AuthorClaas Heuer
Date of publication2015-09-15 08:35:30
MaintainerClaas Heuer <cheuer@tierzucht.uni-kiel.de>
LicenseGPL (>= 2)
URL https://github.com/cheuerde/cpgen
Package repositoryView on CRAN
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cpgen documentation built on May 29, 2017, 9:40 p.m.