cpgen: Parallelized Genomic Prediction and GWAS

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.

Version: 0.1
Depends: R (≥ 3.1.0), Matrix (≥ 1.0-5), pedigreemm (≥ 0.3-3)
Imports: methods, stats
LinkingTo: Rcpp, RcppEigen, RcppProgress
Published: 2015-09-15
Author: Claas Heuer
Maintainer: Claas Heuer <cheuer at tierzucht.uni-kiel.de>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/cheuerde/cpgen
NeedsCompilation: yes
SystemRequirements: C++11
CRAN checks: cpgen results


Reference manual: cpgen.pdf
Package source: cpgen_0.1.tar.gz
Windows binaries: r-devel: cpgen_0.1.zip, r-release: cpgen_0.1.zip, r-oldrel: cpgen_0.1.zip
OS X Mavericks binaries: r-release: cpgen_0.1.tgz, r-oldrel: cpgen_0.1.tgz


Please use the canonical form https://CRAN.R-project.org/package=cpgen to link to this page.