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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.

Versions across snapshots

VersionRepositoryFileSize
1.0.4.2 rolling linux/jammy R-4.5 stmgp_1.0.4.2.tar.gz 514.0 KiB
1.0.4.2 rolling linux/noble R-4.5 stmgp_1.0.4.2.tar.gz 514.0 KiB
1.0.4.2 rolling source/ R- stmgp_1.0.4.2.tar.gz 441.7 KiB
1.0.4.2 latest linux/jammy R-4.5 stmgp_1.0.4.2.tar.gz 514.0 KiB
1.0.4.2 latest linux/noble R-4.5 stmgp_1.0.4.2.tar.gz 514.0 KiB
1.0.4.2 latest source/ R- stmgp_1.0.4.2.tar.gz 441.7 KiB
1.0.4.2 2026-04-26 source/ R- stmgp_1.0.4.2.tar.gz 441.7 KiB
1.0.4.2 2026-04-23 source/ R- stmgp_1.0.4.2.tar.gz 441.7 KiB
1.0.4.2 2026-04-09 windows/windows R-4.5 stmgp_1.0.4.2.zip 505.3 KiB
1.0.4.1 2025-04-20 source/ R- stmgp_1.0.4.1.tar.gz 439.0 KiB

Dependencies (latest)

Depends