DESCRIPTION

Type: Package
Package: CRE
Title: Interpretable Subgroups Identification Through Ensemble Learning of
    Causal Rules
Version: 0.2.0
Authors@R: c(
    person("Naeem", "Khoshnevis", , "nkhoshnevis@g.harvard.edu", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-4315-1426", AFFILIATION = "FASRC")),
    person("Daniela Maria", "Garcia", , "danielagarcia@college.harvard.edu", role = "aut",
           comment = c(ORCID = "0000-0003-3226-3561")),
    person("Riccardo", "Cadei", , "rcadei@hsph.harvard.edu", role = "aut",
           comment = c(ORCID = "0000-0003-2416-8943")),
    person("Kwonsang", "Lee", , "kwonsanglee.stat@gmail.com", role = "aut",
           comment = c(ORCID = "0000-0002-5823-4331")),
    person("Falco Joannes", "Bargagli Stoffi", , "fbargaglistoffi@hsph.harvard.edu", role = "aut",
           comment = c(ORCID = "0000-0002-6131-8165"))
  )
Maintainer: Naeem Khoshnevis <nkhoshnevis@g.harvard.edu>
Description: Provides an interpretable identification of subgroups with
    heterogeneous causal effect. The heterogeneous subgroups are
    discovered through ensemble learning of causal rules. Causal rules are
    highly interpretable if-then statement that recursively partition the
    features space into heterogeneous subgroups. A small number of
    significant causal rules are selected through Stability Selection to
    control for family-wise error rate in the finite sample setting. It
    proposes various estimation methods for the conditional causal effects
    for each discovered causal rule.  It is highly flexible and multiple
    causal estimands and imputation methods are implemented.  Lee, K.,
    Bargagli-Stoffi, F. J., & Dominici, F. (2020). Causal rule ensemble:
    Interpretable inference of heterogeneous treatment effects.  arXiv
    preprint <arXiv:2009.09036>.
License: GPL-3
URL: https://github.com/NSAPH-Software/CRE
BugReports: https://github.com/NSAPH-Software/CRE/issues
Depends: 
    R (>= 3.5.0)
Imports: 
    MASS,
    stats,
    logger,
    gbm, 
    randomForest,
    methods,
    xgboost,
    RRF,
    data.table,
    xtable,
    glmnet,
    bartCause,
    stabs,
    stringr,
    SuperLearner,
    dplyr,
    magrittr,
    ggplot2,
    bcf,
    inTrees
Suggests:
    baggr,
    grf,
    BART,
    gnm,
    covr,
    knitr,
    rmarkdown,
    testthat (>= 3.0.0)
VignetteBuilder: 
    knitr
Copyright: Harvard University
Encoding: UTF-8
Language: en-US
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.1