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: |
1.0.4.2 |
| Depends: |
MASS |
| Published: |
2025-10-03 |
| DOI: |
10.32614/CRAN.package.stmgp |
| Author: |
Masao Ueki [aut, cre] |
| Maintainer: |
Masao Ueki <uekimrsd at nifty.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| SystemRequirements: |
PLINK must be installed |
| Materials: |
README, NEWS |
| CRAN checks: |
stmgp results |
Documentation:
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