Package: abess 0.4.9

abess: Fast Best Subset Selection

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.

Authors:Jin Zhu [aut, cre], Zezhi Wang [aut], Liyuan Hu [aut], Junhao Huang [aut], Kangkang Jiang [aut], Yanhang Zhang [aut], Borui Tang [aut], Shiyun Lin [aut], Junxian Zhu [aut], Canhong Wen [aut], Heping Zhang [aut], Xueqin Wang [aut], spectra contributors [cph]

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Bug tracker:https://github.com/abess-team/abess/issues

On CRAN:

7 exports 6 stars 0.91 score 5 dependencies 69 scripts 909 downloads

Last updated 8 days agofrom:5070b6062c. Checks:OK: 9. Indexed: yes.

Exports:abessabesspcaabessrpcaextractgenerate.datagenerate.matrixgenerate.spc.matrix

Dependencies:latticeMASSMatrixRcppRcppEigen