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>) <> is provided. Covariates can be included in regression model.

Version: 1.0.4
Depends: MASS
Published: 2021-07-18
DOI: 10.32614/CRAN.package.stmgp
Author: Masao Ueki
Maintainer: Masao Ueki <uekimrsd at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
SystemRequirements: PLINK must be installed
Materials: README NEWS
CRAN checks: stmgp results


Reference manual: stmgp.pdf


Package source: stmgp_1.0.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): stmgp_1.0.4.tgz, r-oldrel (arm64): stmgp_1.0.4.tgz, r-release (x86_64): stmgp_1.0.4.tgz, r-oldrel (x86_64): stmgp_1.0.4.tgz
Old sources: stmgp archive


Please use the canonical form to link to this page.