The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Variable selection using the Pivotal Information Criterion.
Sparse regression and classification via the Pivotal Information Criterion (PIC), an alternative to 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 FISTA optimization for the L1, SCAD, and MCP penalties across six response distributions:
| Family | family = |
Response |
|---|---|---|
| Gaussian | "gaussian" |
continuous |
| Binomial | "binomial" |
0/1 binary |
| Poisson | "poisson" |
count |
| Exponential | "exponential" |
positive cont. |
| Gumbel | "gumbel" |
continuous |
| Cox PH | "cox" |
(time, event) |
Under standard sparsity assumptions, the selector achieves a phase transition for exact support recovery, analogous to results in compressed sensing.
Install the released version from CRAN:
install.packages("picreg")Or the development version from GitHub:
# install.packages("remotes")
remotes::install_github("VcMaxouuu/picreg")library(picreg)
data(QuickStartExample)
fit <- pic(QuickStartExample$X, QuickStartExample$y)
fit$selected # names of the selected variables
fit$lambda # PDB-selected lambda (no cross-validation)
summary(fit) # family, penalty, lambda, and non-zero coefficients
coef(fit) # coefficients (sparse matrix, original scale of X)
predict(fit, newx = QuickStartExample$X[1:5, ])The design deliberately mirrors glmnet: a single
pic() fitting function returning an object equipped with
print(), summary(), coef(),
predict(), plot(), and assess()
methods that behave consistently across all six families.
The full walk-through — fitting across all six families, predicting,
visualizing, choosing penalties, and running diagnostics
(phase_transition(), pdb_asymptotic()) — lives
in the package vignette:
vignette("vignette", package = "picreg")Sardy, van Cutsem, and van de Geer. The Pivotal Information Criterion. https://arxiv.org/abs/2603.04172 (doi:10.48550/arXiv.2603.04172)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.