Best Subset Selection in Linear, Logistic and CoxPH Models

An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) . The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.

aic.R:  These functions are used to compute Information Criteria such as AIC, BIC or EBIC from a fitted model object.

bess.one.R:  Best subset selection with a specified model size for generalized linear models and Cox's proportional hazard model.

bess.R:  Best subset selection for generalized linear model and Cox's proportional model. This function is used to determine the subset size k.

coef.bess.R:  This function provides estimated coefficients from a fitted "bess" object or a fitted "bess.one" object.

deviance.bess.R:  This function provides deviance from a fitted "bess" object or a fitted "bess.one" object.

LogLik.bess.R:  This function provides loglikelihood from a fitted "bess" object or a fitted "bess.one" object.

plot.bess.R:  Produces a coefficient profile plot of the coefficient or loss paths for a fitted "bess" object.

predict.bess.R:  This function provides predictions from a fitted "bess" object or a fitted "bess.one" object.

print.bess.R:  Print a summary of the "bess" path at each step along the path.