Package: CIMTx
Type: Package
Title: Causal Inference for Multiple Treatments with a Binary Outcome
Version: 1.2.0
Authors@R: c(
  person("Liangyuan", "Hu", role = "aut", email = "lh707@sph.rutgers.edu"),
  person("Chenyang", "Gu", role = "aut",
         email = "chenyang_gu@alumni.brown.edu"),
  person("Michael", "Lopez", role = "aut", email = "Michael.Lopez@nfl.com"),
  person("Jiayi", "Ji", role = c("aut", "cre"), email = "jjy2876@gmail.com"))
Description: Different methods to conduct causal inference for multiple
        treatments with a binary outcome, including regression
        adjustment, vector matching, Bayesian additive regression
        trees, targeted maximum likelihood and inverse probability of
        treatment weighting using different generalized propensity
        score models such as multinomial logistic regression,
        generalized boosted models and super learner. For more details,
        see the paper by Hu et al. <doi:10.1177/0962280220921909>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.1.2
Imports: nnet (>= 7.3-16), BART (>= 2.9), twang (>= 2.5), arm (>=
        1.2-12), dplyr (>= 1.0.7), Matching (>= 4.9-11), magrittr (>=
        2.0.1), WeightIt (>= 0.12.0), tmle (>= 1.5.0.2), tidyr (>=
        1.1.4), stats, ggplot2 (>= 3.3.5), cowplot (>= 1.1.1), mgcv (>=
        1.8-38), metR (>= 0.11.0), stringr (>= 1.4.0), SuperLearner (>=
        2.0-28), foreach (>= 1.5.1), doParallel (>= 1.0.16)
NeedsCompilation: no
Packaged: 2026-10-13 13:37:38 UTC; root
Author: Liangyuan Hu [aut], Chenyang Gu [aut], Michael Lopez [aut],
        Jiayi Ji [aut, cre]
Maintainer: Jiayi Ji <jjy2876@gmail.com>
Config/pak/sysreqs: libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev
        make libicu-dev libjpeg-dev libpng-dev libssl-dev libproj-dev
        libsqlite3-dev libudunits2-dev
Repository: https://jiayiji.r-universe.dev
Date/Publication: 2022-06-24 06:40:02 UTC
RemoteUrl: https://github.com/cran/CIMTx
RemoteRef: HEAD
RemoteSha: 2bb67b80b853b17533139faac3450f799d45a844