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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("cpgen")

0.1 by Claas Heuer, 3 years ago


https://github.com/cheuerde/cpgen


Browse source code at https://github.com/cran/cpgen


Authors: Claas Heuer


Documentation:   PDF Manual  


GPL (>= 2) license


Imports methods, stats

Depends on Matrix, pedigreemm

Linking to Rcpp, RcppEigen, RcppProgress

System requirements: C++11


See at CRAN