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picreg  

Variable Selection using the Pivotal Information Criterion
View on CRAN: Click here


Download and install picreg package within the R console
Install from CRAN:
install.packages("picreg")

Install from Github:
library("remotes")
install_github("cran/picreg")

Install by package version:
library("remotes")
install_version("picreg", "0.1.4")



Attach the package and use:
library("picreg")
Maintained by
Maxime van Cutsem
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-03
Latest Update: 2026-06-04
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>.
How to cite:
Maxime van Cutsem (2026). picreg: Variable Selection using the Pivotal Information Criterion. R package version 0.1.4, https://cran.r-project.org/web/packages/picreg. Accessed 29 Oct. 2026.
Previous versions and publish date:
(2026-10-20 14:20), 0.1.2 (2026-06-03 15:40), 0.1.3 (2026-06-04 16:50), 0.1.4 (2026-07-20 14:20)
Other packages that cited picreg R package
View picreg citation profile
Other R packages that picreg depends, imports, suggests or enhances
Complete documentation for picreg
Functions, R codes and Examples using the picreg R package
Full picreg package functions and examples
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