plsmselect
Linear and Smooth Predictor Modelling with Penalisation and Variable Selection
Fit a model with potentially many linear and smooth predictors. Interaction effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties we use iterative steps alternating between using linear predictors (lasso) and smooth predictors (generalised additive model).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.0 |
rolling linux/jammy R-4.5 | plsmselect_0.2.0.tar.gz |
155.4 KiB |
0.2.0 |
rolling linux/noble R-4.5 | plsmselect_0.2.0.tar.gz |
155.4 KiB |
0.2.0 |
rolling source/ R- | plsmselect_0.2.0.tar.gz |
96.8 KiB |
0.2.0 |
latest linux/jammy R-4.5 | plsmselect_0.2.0.tar.gz |
155.4 KiB |
0.2.0 |
latest linux/noble R-4.5 | plsmselect_0.2.0.tar.gz |
155.4 KiB |
0.2.0 |
latest source/ R- | plsmselect_0.2.0.tar.gz |
96.8 KiB |
0.2.0 |
2026-04-26 source/ R- | plsmselect_0.2.0.tar.gz |
96.8 KiB |
0.2.0 |
2026-04-23 source/ R- | plsmselect_0.2.0.tar.gz |
96.8 KiB |
0.2.0 |
2026-04-09 windows/windows R-4.5 | plsmselect_0.2.0.zip |
158.9 KiB |
0.2.0 |
2025-04-20 source/ R- | plsmselect_0.2.0.tar.gz |
96.8 KiB |