cpgen-package


The package offers a variety of functions that are frequently being used in genomic prediction
and genomewide association studies. The package is based on Rcpp and RcppEigen, hence all routines
are implemented using the matrix algebra library Eigen.
The main emphasis of the package lies in parallel computing which is realized by C++ functions making
use of OpenMP. 


 package

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.

cpgen-package: cpgen - Parallel genomic evaluations

Description

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