Package: knockoff 0.3.6

knockoff: The Knockoff Filter for Controlled Variable Selection

The knockoff filter is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. For more information, see the website below and the accompanying paper: Candes et al., "Panning for gold: model-X knockoffs for high-dimensional controlled variable selection", J. R. Statist. Soc. B (2018) 80, 3, pp. 551-577.

Authors:Rina Foygel Barber [ctb], Emmanuel Candes [ctb], Lucas Janson [ctb], Evan Patterson [aut], Matteo Sesia [aut, cre]

knockoff_0.3.6.tar.gz
knockoff_0.3.6.zip(r-4.5)knockoff_0.3.6.zip(r-4.4)knockoff_0.3.6.zip(r-4.3)
knockoff_0.3.6.tgz(r-4.4-any)knockoff_0.3.6.tgz(r-4.3-any)
knockoff_0.3.6.tar.gz(r-4.5-noble)knockoff_0.3.6.tar.gz(r-4.4-noble)
knockoff_0.3.6.tgz(r-4.4-emscripten)knockoff_0.3.6.tgz(r-4.3-emscripten)
knockoff.pdf |knockoff.html✨
knockoff/json (API)
NEWS

# Install 'knockoff' in R:
install.packages('knockoff', repos = c('https://msesia.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.30 score 2 stars 5 packages 224 scripts 445 downloads 21 exports 14 dependencies

Last updated 2 years agofrom:14490c6500. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-winNOTENov 12 2024
R-4.5-linuxNOTENov 12 2024
R-4.4-winNOTENov 12 2024
R-4.4-macNOTENov 12 2024
R-4.3-winOKNov 12 2024
R-4.3-macOKNov 12 2024

Exports:create.fixedcreate.gaussiancreate.second_ordercreate.solve_asdpcreate.solve_equicreate.solve_sdpknockoff.filterknockoff.thresholdstat.forward_selectionstat.glmnet_coefdiffstat.glmnet_lambdadiffstat.glmnet_lambdasmaxstat.lasso_coefdiffstat.lasso_coefdiff_binstat.lasso_lambdadiffstat.lasso_lambdadiff_binstat.lasso_lambdasmaxstat.lasso_lambdasmax_binstat.random_foreststat.sqrt_lassostat.stability_selection

Dependencies:codetoolscorpcorforeachglmnetgtoolsiteratorslatticeMatrixRcppRcppEigenRdsdpRSpectrashapesurvival

Advanced Usage of the Knockoff Filter for R

Rendered fromadvanced.Rmdusingknitr::rmarkdownon Nov 12 2024.

Last update: 2022-08-15
Started: 2017-10-17

Controlled variable Selection with Fixed-X Knockoffs

Rendered fromfixed.Rmdusingknitr::rmarkdownon Nov 12 2024.

Last update: 2022-08-15
Started: 2017-10-17

Controlled variable Selection with Model-X Knockoffs

Rendered fromknockoff.Rmdusingknitr::rmarkdownon Nov 12 2024.

Last update: 2022-08-15
Started: 2017-10-17

Readme and manuals

Help Manual

Help pageTopics
Fixed-X knockoffscreate.fixed
Model-X Gaussian knockoffscreate.gaussian
Second-order Gaussian knockoffscreate.second_order
Relaxed optimization for fixed-X and Gaussian knockoffscreate.solve_asdp
Optimization for equi-correlated fixed-X and Gaussian knockoffscreate.solve_equi
Optimization for fixed-X and Gaussian knockoffscreate.solve_sdp
knockoff: A package for controlled variable selectionknockoff
The Knockoff Filterknockoff.filter
Threshold for the knockoff filterknockoff.threshold
Print results for the knockoff filterprint.knockoff.result
Importance statistics based on forward selectionstat.forward_selection
Importance statistics based on a GLM with cross-validationstat.glmnet_coefdiff
Importance statistics based on a GLMstat.glmnet_lambdadiff
GLM statistics for knockoffstat.glmnet_lambdasmax
Importance statistics based the lasso with cross-validationstat.lasso_coefdiff
Importance statistics based on regularized logistic regression with cross-validationstat.lasso_coefdiff_bin
Importance statistics based on the lassostat.lasso_lambdadiff
Importance statistics based on regularized logistic regressionstat.lasso_lambdadiff_bin
Penalized linear regression statistics for knockoffstat.lasso_lambdasmax
Penalized logistic regression statistics for knockoffstat.lasso_lambdasmax_bin
Importance statistics based on random forestsstat.random_forest
Importance statistics based on the square-root lassostat.sqrt_lasso
Importance statistics based on stability selectionstat.stability_selection