picreg
Variable Selection using the Pivotal Information Criterion
Sparse regression and classification via the Pivotal Information Criterion (PIC), an alternative to the Bayesian Information Criterion (BIC), cross-validation, and Lasso-based tuning. The regularisation parameter is selected from a pivotal null-distribution statistic, eliminating the need for cross-validation and yielding sharper support recovery. Provides Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) optimisation for the L1, Smoothly Clipped Absolute Deviation (SCAD), and Minimax Concave Penalty (MCP) penalties across six response distributions: Gaussian, binomial, Poisson, exponential, Gumbel, and Cox. Under standard sparsity assumptions, the selector achieves a phase transition for exact support recovery, analogous to results in compressed sensing. See Sardy, van Cutsem and van de Geer (2026) <doi:10.48550/arXiv.2603.04172>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.2 |
rolling linux/jammy R-4.5 | picreg_0.1.2.tar.gz |
836.5 KiB |
0.1.2 |
rolling linux/noble R-4.5 | picreg_0.1.2.tar.gz |
844.4 KiB |
0.1.2 |
rolling source/ R- | picreg_0.1.2.tar.gz |
650.7 KiB |
0.1.2 |
latest linux/jammy R-4.5 | picreg_0.1.2.tar.gz |
836.5 KiB |
0.1.2 |
latest linux/noble R-4.5 | picreg_0.1.2.tar.gz |
844.4 KiB |
0.1.2 |
latest source/ R- | picreg_0.1.2.tar.gz |
650.7 KiB |
0.1.2 |
2026-04-23 source/ R- | picreg_0.1.2.tar.gz |
0 B |