Knockoffs.jl

This is a Julia implementation of the knockoff filter. The knockoff filter is a general framework for controlling the false discovery rate when performing variable selection. As the name suggests, the knockoff filter operates by manufacturing knockoff variables that are cheap — their construction does not require collecting any new data — and are designed to mimic the correlation structure found within the original variables. The knockoffs serve as negative controls and they allow one to identify the truly important predictors, while controlling the false discovery rate (FDR) — the expected fraction of false discoveries among all discoveries.

Installation

Within Julia,

using Pkg
Pkg.add("Knockoffs")

This package supports Julia v1.8+.

Manual Outline

• Fixed-X knockoffs
• • Generate knockoffs
  • LASSO example
• Model-X knockoffs
• • Gaussian model-X knockoffs with known mean and covariance
  • Second order knockoffs
  • Approximate construction for speed
  • Multiple knockoffs
  • LASSO example
• Group Knockoffs
• • Gaussian model-X group knockoffs with known mean and covariance
  • Define group memberships
  • Generating group knockoffs
  • Second order group knockoffs
  • Group knockoffs based on conditional independence assumption
  • Lasso Example
  • Conclusion
• KnockoffScreen knockoffs
• • Step 0: Prepare example data
  • Step 1: Generate Knockoffs
  • Step 2: Examine knockoff statistics
• Ghost Knockoffs
• HMM Knockoffs
• • fastPHASE HMM knockoffs
  • SHAPEIT HMM knockoffs
• IPAD knockoffs
• • Test1: Simulate under factored model
  • Test2: Try $X$ that's not a factored model
  • Test3: Simulate with gnomAD panel
• Calling from R and/or Python
• API