lmSubsets
Exact Variable-Subset Selection in Linear Regression
Exact and approximation algorithms for variable-subset selection in ordinary linear regression models. Either compute all submodels with the lowest residual sum of squares, or determine the single-best submodel according to a pre-determined statistical criterion. Hofmann et al. (2020) <doi:10.18637/jss.v093.i03>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.5-4 |
rolling linux/jammy R-4.5 | lmSubsets_0.5-4.tar.gz |
788.8 KiB |
0.5-4 |
rolling linux/noble R-4.5 | lmSubsets_0.5-4.tar.gz |
787.5 KiB |
0.5-4 |
rolling source/ R- | lmSubsets_0.5-4.tar.gz |
1.0 MiB |
0.5-4 |
latest linux/jammy R-4.5 | lmSubsets_0.5-4.tar.gz |
788.8 KiB |
0.5-4 |
latest linux/noble R-4.5 | lmSubsets_0.5-4.tar.gz |
787.5 KiB |
0.5-4 |
latest source/ R- | lmSubsets_0.5-4.tar.gz |
1.0 MiB |
0.5-4 |
2026-04-26 source/ R- | lmSubsets_0.5-4.tar.gz |
1.0 MiB |
0.5-4 |
2026-04-23 source/ R- | lmSubsets_0.5-4.tar.gz |
1.0 MiB |
0.5-4 |
2026-04-09 windows/windows R-4.5 | lmSubsets_0.5-4.zip |
854.7 KiB |
0.5-2 |
2025-04-20 source/ R- | lmSubsets_0.5-2.tar.gz |
1.0 MiB |