abess

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, Ising model reconstruction doi:10.1080/01621459.2025.2571245, (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.

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