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", 2016, .


News

knockoff 0.3.2 (08/03/2018)

Fixes:

  • Fixed bug that caused incorrect knockoff statistics in the presence of knockoff copies identical to their own original variable.

Minor changes:

  • Changed scaling of variables for computation of importance measures.

knockoff 0.3.1.1 (06/28/2018)

Minor changes:

  • Improved algorithm for solving SDP
  • Improved algorithm for solving ASDP
  • Returning X instead of throwing error in Gaussian knockoffs, if covariance matrix is not positive-definite

Documentation:

  • Minor improvements to package description file

knockoff 0.3.0 (10/17/2017)

Features:

  • Added support for Model-X knockoffs
  • Added importance statistics
  • Native support for SDP knockoffs (no need to call Python)

Major changes:

  • Model-X knockoffs are used by default
  • Cross-validated lasso statistics are used by default
  • SDP knockoffs are used by default
  • Offset 1 is used by default

Documentation:

  • Updated and expanded vignettes

knockoff 0.2.1

Documentation:

  • Add vignette showing how to analyze a real data set (on HIV drug resistance), including all the preprocessing steps.

knockoff 0.2 (02/04/2015)

Changes:

  • The knockoff procedure is now fully deterministic by default. Randomization can be enabled if desired.

Fixes:

  • Fix numerical precision bug in equicorrelated knockoff creation

knockoff 0.1.1 (12/19/2014)

Features:

  • Expose the optional 'nlambda' parameter for lasso statistics

Fixes:

  • Better documentation for SDP knockoffs
  • Minor bug fixes

knockoff 0.1 (12/05/2014)

Initial release!

Reference manual

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install.packages("knockoff")

0.3.2 by Matteo Sesia, 9 months ago


https://web.stanford.edu/group/candes/knockoffs/index.html


Browse source code at https://github.com/cran/knockoff


Authors: Rina Foygel Barber [ctb] (Development of the original Fixed-X Knockoffs) , Emmanuel Candes [ctb] (Development of Model-X Knockoffs and original Fixed-X Knockoffs) , Lucas Janson [ctb] (Development of Model-X Knockoffs) , Evan Patterson [aut] (Original R package for the original Fixed-X Knockoffs) , Matteo Sesia [aut, cre] (R package for Model-X Knockoffs)


Documentation:   PDF Manual  


GPL-3 license


Imports Rdsdp, Matrix, corpcor, glmnet, RSpectra, gtools, utils

Depends on methods, stats

Suggests knitr, testthat, rmarkdown, lars, ranger, stabs, flare, doMC, parallel


Suggested by CBDA.


See at CRAN