stmgp: Rapid and Accurate Genetic Prediction Modeling for Genome-Wide Association or Whole-Genome Sequencing Study Data

Rapidly build accurate genetic prediction models for genome-wide association or whole-genome sequencing study data by smooth-threshold multivariate genetic prediction (STMGP) method. Variable selection is performed using marginal association test p-values with an optimal p-value cutoff selected by Cp-type criterion. Quantitative and binary traits are modeled respectively via linear and logistic regression models. A function that works through PLINK software (Purcell et al. 2007 <doi:10.1086/519795>, Chang et al. 2015 <doi:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is provided. Covariates can be included in regression model.

Version: 1.0
Depends: MASS
Published: 2017-03-21
Author: Masao Ueki
Maintainer: Masao Ueki <uekimrsd at nifty.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
SystemRequirements: PLINK must be installed
CRAN checks: stmgp results

Downloads:

Reference manual: stmgp.pdf
Package source: stmgp_1.0.tar.gz
Windows binaries: r-devel: stmgp_1.0.zip, r-release: stmgp_1.0.zip, r-oldrel: stmgp_1.0.zip
OS X El Capitan binaries: r-release: stmgp_1.0.tgz
OS X Mavericks binaries: r-oldrel: stmgp_1.0.tgz

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