{ "_id": "6a5e08879baadeef8ab56966", "Package": "picreg", "Type": "Package", "Title": "Variable Selection using the Pivotal Information Criterion", "Version": "0.1.4", "Date": "2026-07-20", "Authors@R": "c(\nperson(\"Maxime\", \"van Cutsem\",\nrole = c(\"aut\", \"cre\"),\nemail = \"maxime.vancutsem@unige.ch\"),\nperson(\"Sylvain\", \"Sardy\",\nrole = \"aut\",\nemail = \"sylvain.sardy@unige.ch\"))", "Description": "Sparse regression and classification via the Pivotal\nInformation Criterion (PIC), an alternative to the Bayesian\nInformation Criterion (BIC), cross-validation, and Lasso-based\ntuning. The regularization parameter is selected from a pivotal\nnull-distribution statistic, eliminating the need for\ncross-validation and yielding sharper support recovery.\nProvides Fast Iterative Shrinkage-Thresholding Algorithm\n(FISTA) optimization for the L1, Smoothly Clipped Absolute\nDeviation (SCAD), and Minimax Concave Penalty (MCP) penalties\nacross six response distributions: Gaussian, binomial, Poisson,\nexponential, Gumbel, and Cox. 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