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.
|Date of publication||2015-09-15 08:35:30|
|Maintainer||Claas Heuer <firstname.lastname@example.org>|
|License||GPL (>= 2)|
ccolmv: Colwise means or variances
cCV: Generate phenotype vectors for cross validation
cGBLUP: Genomic BLUP
cgrm: Genomic Relationship Matrices
cgrm.A: Additive Genomic Relationship Matrix
cgrm.D: Dominance Genomic Relationship Matrix
cGWAS: Genomewide Association Study
cGWAS.emmax: Genomewide Association Study - EMMAX
check_openmp: Check OpenMP-support.
clmm: Linear Mixed Models using Gibbs Sampling
cpgen-package: cpgen - Parallel genomic evaluations
cpgen-parallel: Multithreading using 'cpgen'
cpowop: Square matrix power operator
crossprodop: (Parallel) Matrix product operator
cscanx: Read in a matrix from a file
cSSBR: Single Step Bayesian Regression
cSSBR.setup: Preparing Model terms for Single Step Bayesian Regression
get_cor: Compute the prediction accuracy from Cross Validition
get_max_threads: Get the maximum number of threads available
get_num_threads: Get the number of threads for 'cpgen'
get_pred: Extract predictions vectors of an object returned by 'clmm'...
rand_data: Generate random data for test purposes
set_num_threads: Set the number of OpenMP threads used by the functions of...