CovSelHigh: Model-Free Covariate Selection in High Dimensions

Model-free selection of covariates in high dimensions under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011) <doi:10.1093/biomet/asr041> and VanderWeele and Shpitser (2011) <doi:10.1111/j.1541-0420.2011.01619.x>. Confounder selection can be performed via either Markov/Bayesian networks, random forests or LASSO.

Version: 1.1.1
Depends: R (≥ 2.14.0)
Imports: bnlearn, MASS, bindata, Matching, doRNG, glmnet, randomForest, foreach, xtable, doParallel, bartMachine, tmle
Published: 2017-07-03
Author: Jenny Häggström
Maintainer: Jenny Häggström <jenny.haggstrom at umu.se>
License: GPL-3
NeedsCompilation: no
CRAN checks: CovSelHigh results

Downloads:

Reference manual: CovSelHigh.pdf
Package source: CovSelHigh_1.1.1.tar.gz
Windows binaries: r-devel: CovSelHigh_1.1.1.zip, r-release: CovSelHigh_1.1.1.zip, r-oldrel: CovSelHigh_1.1.1.zip
OS X El Capitan binaries: r-release: CovSelHigh_1.1.1.tgz
OS X Mavericks binaries: r-oldrel: CovSelHigh_1.1.1.tgz
Old sources: CovSelHigh archive

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