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Selection Threshold Optimized Empirically via Splitting
Marinela Capanu, Mihai Giurcanu, Colin Begg, and Mithat Gonen
A variable selection procedure for low to moderate size linear regressions models. This method repeatedly splits the data into two sets, one for estimation and one for validation, to obtain an empirically optimized threshold which is then used to screen for variables to include in the final model.

Marinela Capanu [aut, cre],
Mihai Giurcanu [aut, ctb],
Colin Begg [aut],
Mithat Gonen [aut]
c(person("Marinela", "Capanu", role = c("aut", "cre"), email = "capanum@mskcc.org"),
person("Mihai", "Giurcanu", role = c("aut", "ctb")),
person("Colin", "Begg", role = "aut"),
person("Mithat", "Gonen", role = "aut"))
Implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models).

Selection of Training Populations by Genetic Algorithm
Can be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set.

Can be utilized to select a (test data) calibrated training population in high dimensional prediction problems.

To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.
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