README.md
In haimbar/SEMMS: Variable selection in Generalized Linear Models - an empirical Bayes approach
SEMMS
An R package to perform variable selection in the 'large P, small N' setting. It supports normal, binary, and count response types (generalized linear models). It allows the user to specify locked-in variables - predictors which must be included in the model.
See the vignettes folder for detailed examples. In particular, SEMMS.pdf and classificationWithSEMMS.pdf.
haimbar/SEMMS documentation built on Dec. 20, 2021, 2:44 p.m.
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SEMMS
An R package to perform variable selection in the 'large P, small N' setting. It supports normal, binary, and count response types (generalized linear models). It allows the user to specify locked-in variables - predictors which must be included in the model.
See the vignettes folder for detailed examples. In particular, SEMMS.pdf and classificationWithSEMMS.pdf.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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