l0: Feature selection subroutine

In cbl: Causal Discovery under a Confounder Blanket

Feature selection subroutine

Description

This function fits a potentially sparse supervised learning model and returns a bit vector indicating which features were selected.

Usage

l0(x, y, s, params, ...)

Arguments

x	Design matrix.
y	Outcome vector.
s	Regression method. Current options are "lasso" or "boost".
params	Optional list of parameters to use when s = "boost".
...	Extra parameters to be passed to the feature selection subroutine.

cbl documentation built on Dec. 28, 2022, 1:55 a.m.