Package: CovSelHigh
Version: 1.1.1
Author: Jenny Häggström
Maintainer: Jenny Häggström <jenny.haggstrom@umu.se>
Depends: R (>= 2.14.0)
Imports: bnlearn,MASS, bindata, Matching, doRNG, glmnet,
        randomForest,foreach,xtable, doParallel, bartMachine, tmle
Title: Model-Free Covariate Selection in High Dimensions
Description: 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.
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2017-07-03 08:37:03 UTC; jennyhaggstrom
Repository: CRAN
Date/Publication: 2017-07-03 09:35:40 UTC
Built: R 4.0.2; ; 2020-07-16 21:04:44 UTC; unix
