Fast Best Subset Selection
abess: Fast Best Subset Selection
Adaptive best subset selection (for generalized linear model)
Adaptive best subset selection for principal component analysis
Adaptive best subset selection for robust principal component analysis
Extract Model Coefficients from a fitted "abess" object.
Extract Sparse Loadings from a fitted "abesspca" object.
Extract sparse component from a fitted "abessrpca" object.
Extract the deviance from a fitted "abess" object.
Extract one model from a fitted "abess" object.
Generate simulated data
Generate matrix composed of a sparse matrix and low-rank matrix
Generate matrix with sparse principal component
Creat plot from a fitted "abess" object
Creat plot from a fitted "abess" object
Creat plot from a fitted "abessrpca" object
Make predictions from a fitted "abess" object.
Print method for a fitted "abess" object
Print method for a fitted "abesspca" object
Print method for a fitted "abessrpca" object
Extremely efficient toolkit for solving the best subset selection problem <https://www.jmlr.org/papers/v23/21-1060.html>. This package is its R interface. The package implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection <doi:10.1287/ijoc.2022.1241> and sure independence screening <doi:10.1111/j.1467-9868.2008.00674.x> are also provided.
Useful links