cpgen
0.1Parallelized Genomic Prediction and GWAS
Overview
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.
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Documentation
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- 58%
- Documented parameters
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- Return-value docs
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- References docs
- 36%
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Code & Tests
People & History
1 release. R releases are shown for context.
- RR 4.0.0 released · 2020-04-24
- archivedRemoved from CRAN2019-07-02misuse of package= for PACKAGE= was not corrected despite reminder
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- RR 3.3.0 released · 2016-05-03
- 0.12015-09-15
- RR 3.2.0 released · 2015-04-16
Package metadata
- Total releases
- 1
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.1.0
- Download size
- not tracked yet
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