CRAN/E | CIMTx

CIMTx

Causal Inference for Multiple Treatments with a Binary Outcome

Installation

About

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.

Key Metrics

Version 1.2.0
Published 2022-06-24 665 days ago
Needs compilation? no
License MIT
License File
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Maintainer

Maintainer

Jiayi Ji

jjy2876@gmail.com

Authors

Liangyuan Hu

aut

Chenyang Gu

aut

Michael Lopez

aut

Jiayi Ji

aut / cre

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

CIMTx archive

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