Covariance Regression with Random Forests
c(person(given = "Cansu", family = "Alakus", role = c("aut", "cre"), email = "cansu.alakus@hec.ca"),
person(given = "Denis", family = "Larocque", role = c("aut"), email = "denis.larocque@hec.ca"),
person(given = "Aurelie", family = "Labbe", role = c("aut"), email = "aurelie.labbe@hec.ca"),
person(given = "Hemant", family = "Ishwaran", role = c("ctb"), comment = "Author of included 'randomForestSRC' codes"),
person(given = "Udaya B.", family = "Kogalur", role = c("ctb"), comment = "Author of included 'randomForestSRC' codes"))
Covariance Regression with Random Forests ('CovRegRF') is a
random forest method for estimating the covariance matrix of a
multivariate response given a set of covariates. Random forest trees
are built with a new splitting rule which is designed to maximize the
distance between the sample covariance matrix estimates of the child
nodes. The method is described in Alakus et al. (2022)
<arXiv:2209.08173>. 'CovRegRF' uses 'randomForestSRC' package
(Ishwaran and Kogalur, 2022)
<https://cran.r-project.org/package=randomForestSRC> by freezing at the
version 3.1.0. The custom splitting rule feature is utilised to apply the
proposed splitting rule.
Cansu Alakus [aut, cre],
Denis Larocque [aut],
Aurelie Labbe [aut],
Hemant Ishwaran [ctb] (Author of included 'randomForestSRC' codes),
Udaya B. Kogalur [ctb] (Author of included 'randomForestSRC' codes)

Model-free selection of covariates 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). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.

bnlearn,MASS, bindata, Matching, doRNG, glmnet,
randomForest,foreach,xtable, doParallel
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.