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picreg

Variable Selection using the Pivotal Information Criterion

v0.1.3 · Jun 4, 2026 · GPL-2

Description

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>.

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OK 6 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Jun 4, 2026

Dependency Network

Dependencies Reverse dependencies future future.apply Matrix Rcpp picreg

Version History

2 tracked
updated 0.1.3 ← 0.1.2 diff Jun 4, 2026
new 0.1.2 Jun 3, 2026