Package: lassoinf 0.1.0
lassoinf: Selective Inference for the Randomized Lasso
Confidence intervals and p-values for parameters chosen by the (randomized) lasso, valid after selection. Implements the polyhedral approach of Lee, Sun, Sun and Taylor (2016) <doi:10.1214/15-AOS1371>, the randomized response of Tian and Taylor (2018) <doi:10.1214/17-AOS1564> and the exact randomized inference of Panigrahi, Fry and Taylor (2024) <doi:10.1093/biomet/asae019>, with the truncated bivariate normal computations of Liu (2023) <doi:10.48550/arXiv.2308.10346>. Includes inference for user-specified estimands, screening of inactive variables and problems fitted by 'glmnet'. Computations use a 'C++' backend with matrix-free linear operators for wide designs.
Authors:
lassoinf_0.1.0.tar.gz
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lassoinf_0.1.0.tgz(r-4.6-x86_64)lassoinf_0.1.0.tgz(r-4.6-arm64)lassoinf_0.1.0.tgz(r-4.5-x86_64)lassoinf_0.1.0.tgz(r-4.5-arm64)
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lassoinf_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
lassoinf/json (API)
| # Install 'lassoinf' in R: |
| install.packages('lassoinf', repos = c('https://jonathan-taylor-stanford.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jonathan-taylor/lassoinf/issues
Last updated from:34ba9fb465. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 141 | ||
| linux-devel-x86_64 | OK | 164 | ||
| source / vignettes | OK | 336 | ||
| linux-release-arm64 | OK | 139 | ||
| linux-release-x86_64 | OK | 156 | ||
| macos-release-arm64 | OK | 324 | ||
| macos-release-x86_64 | OK | 286 | ||
| macos-oldrel-arm64 | OK | 256 | ||
| macos-oldrel-x86_64 | OK | 357 | ||
| windows-devel-arm64 | OK | 449 | ||
| windows-devel-x86_64 | OK | 197 | ||
| windows-release-arm64 | OK | 426 | ||
| windows-release-x86_64 | OK | 123 | ||
| windows-oldrel-x86_64 | OK | 161 | ||
| wasm-release | OK | 103 |
Exports:AffineConstraintscontrast_estimandcontrast_inferencecovariance_estimandcustom_estimand_inferencedense_operatorDiscreteFamilyestimand_summaryestimand_valueglmnet_inferenceglmnet_problemglmnet_problem_from_fitglmnet_scalinginactive_summarykkt_violationLassoInferenceScreenedSelectionSelectionCoordinatestruncated_normal_inferenceTruncatedGaussianTruncBivariateNormalWeightedGaussianFamilyxtvx_operator
Last update: 2026-10-20
Started: 2026-10-20
Last update: 2026-10-20
Started: 2026-10-20
Last update: 2026-10-20
Started: 2026-10-20
Last update: 2026-10-20
Started: 2026-10-20
Last update: 2026-10-20
Started: 2026-10-20
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| C++ classes | AffineConstraints DiscreteFamily TruncatedGaussian WeightedGaussianFamily |
| Estimand specifications | contrast_estimand covariance_estimand estimand_value |
| Selective inference for a contrast estimand | contrast_inference |
| Selective inference for an estimand given by its covariance with the score | custom_estimand_inference |
| Linear operators | dense_operator xtvx_operator |
| Table of selective inference results | estimand_summary |
| Selective inference after a glmnet fit | glmnet_inference |
| The bounded LASSO problem glmnet solved | glmnet_problem glmnet_problem_from_fit |
| glmnet's column scales | glmnet_scaling |
| Selective inference for inactive variables | inactive_summary |
| KKT violation of the bounded LASSO | kkt_violation |
| LassoInference Class | LassoInference |
| ScreenedSelection Class | ScreenedSelection |
| SelectionCoordinates Class | SelectionCoordinates |
| Truncated normal inference from the polyhedral lemma | truncated_normal_inference |
| TruncBivariateNormal Class | TruncBivariateNormal |
