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>.
| Version: |
0.1.2 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
stats, graphics, grDevices, parallel, future, future.apply, Rcpp (≥ 1.0.10) |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown, xfun, glmnet |
| Published: |
2026-06-03 |
| DOI: |
10.32614/CRAN.package.picreg (may not be active yet) |
| Author: |
Maxime van Cutsem [aut, cre],
Sylvain Sardy [aut] |
| Maintainer: |
Maxime van Cutsem <maxime.vancutsem at unige.ch> |
| BugReports: |
https://github.com/VcMaxouuu/picreg/issues |
| License: |
GPL-2 |
| URL: |
https://github.com/VcMaxouuu/picreg |
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++17 |
| Materials: |
README |
| CRAN checks: |
picreg results [issues need fixing before 2026-06-17] |
Documentation:
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