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:Jonathan Taylor [aut, cre, cph], Kevin Fry [aut], Snigdha Panigrahi [aut], Sifan Liu [ctb, cph]

lassoinf_0.1.0.tar.gz
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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

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

2.98 score 19 scripts 58 downloads 23 exports 4 dependencies

Last updated from:34ba9fb465. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK141
linux-devel-x86_64OK164
source / vignettesOK336
linux-release-arm64OK139
linux-release-x86_64OK156
macos-release-arm64OK324
macos-release-x86_64OK286
macos-oldrel-arm64OK256
macos-oldrel-x86_64OK357
windows-devel-arm64OK449
windows-devel-x86_64OK197
windows-release-arm64OK426
windows-release-x86_64OK123
windows-oldrel-x86_64OK161
wasm-releaseOK103

Exports:AffineConstraintscontrast_estimandcontrast_inferencecovariance_estimandcustom_estimand_inferencedense_operatorDiscreteFamilyestimand_summaryestimand_valueglmnet_inferenceglmnet_problemglmnet_problem_from_fitglmnet_scalinginactive_summarykkt_violationLassoInferenceScreenedSelectionSelectionCoordinatestruncated_normal_inferenceTruncatedGaussianTruncBivariateNormalWeightedGaussianFamilyxtvx_operator

Dependencies:mvtnormR6RcppRcppEigen

Inference after a glmnet fit: carving for logistic regression
Carving as randomization | Data | Select on half of the data with glmnet | Inference | Coverage over repeated experiments

Last update: 2026-10-20
Started: 2026-10-20

Inference after a glmnet fit without randomization
Setup | Data and fit | Inference | For a gaussian response | Coverage over repeated experiments | Reference

Last update: 2026-10-20
Started: 2026-10-20

Randomized LASSO for a Gaussian linear model
Randomization and the proportion used for selection | Data | Fit the LASSO on the randomized response | Inference | Coverage over repeated experiments

Last update: 2026-10-20
Started: 2026-10-20

Screening inactive variables
The selection event in two coordinates | Data: a wide design | Fit the LASSO on the randomized data | Screen the top 10 inactive variables | Ignoring the screening step | The LASSO-solution basis | First-dropped versus exact conditioning | Coverage over repeated experiments

Last update: 2026-10-20
Started: 2026-10-20

Surrogate screening: swapping selected and screened variables
The swapped coefficient as a contrast | Setup | Swap estimands | Example 1: independent features | Does screening matter? | Example 2: correlated features and surrogates

Last update: 2026-10-20
Started: 2026-10-20

Readme and manuals

Help Manual

Help pageTopics
C++ classesAffineConstraints DiscreteFamily TruncatedGaussian WeightedGaussianFamily
Estimand specificationscontrast_estimand covariance_estimand estimand_value
Selective inference for a contrast estimandcontrast_inference
Selective inference for an estimand given by its covariance with the scorecustom_estimand_inference
Linear operatorsdense_operator xtvx_operator
Table of selective inference resultsestimand_summary
Selective inference after a glmnet fitglmnet_inference
The bounded LASSO problem glmnet solvedglmnet_problem glmnet_problem_from_fit
glmnet's column scalesglmnet_scaling
Selective inference for inactive variablesinactive_summary
KKT violation of the bounded LASSOkkt_violation
LassoInference ClassLassoInference
ScreenedSelection ClassScreenedSelection
SelectionCoordinates ClassSelectionCoordinates
Truncated normal inference from the polyhedral lemmatruncated_normal_inference
TruncBivariateNormal ClassTruncBivariateNormal