Variable Selection using the Pivotal Information Criterion R package picreg version 0.1.4 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 regularization 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) optimization 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>. Software Depends: R (>= 3.6.0) Imports: stats, graphics, grDevices, parallel, future, future.apply, Matrix, Rcpp (>= 1.0.10) LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), knitr, rmarkdown, xfun, glmnet Maxime van Cutsem <maxime.vancutsem@unige.ch> Comprehensive R Archive Network (CRAN) Maxime van Cutsem [aut, cre], Sylvain Sardy [aut] GPL-2 2026-07-20 application/tgz https://CRAN.R-project.org/package=picreg doi:10.32614/CRAN.package.picreg