CausalModels: Causal Inference Modeling for Estimation of Causal Effects

Provides an array of statistical models common in causal inference such as standardization, IP weighting, propensity matching, outcome regression, and doubly-robust estimators. Estimates of the average treatment effects from each model are given with the standard error and a 95% Wald confidence interval (Hernan, Robins (2020) <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>).

Version: 0.2.0
Imports: stats, causaldata, boot, multcomp, geepack
Published: 2022-11-23
Author: Joshua Anderson [aut, cre, cph], Cyril Rakovski [rev], Yesha Patel [rev], Erin Lee [rev]
Maintainer: Joshua Anderson <jwanderson198 at gmail.com>
BugReports: https://github.com/ander428/CausalModels/issues
License: GPL-3
URL: https://github.com/ander428/CausalModels
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: CausalModels results

Documentation:

Reference manual: CausalModels.pdf

Downloads:

Package source: CausalModels_0.2.0.tar.gz
Windows binaries: r-devel: CausalModels_0.2.0.zip, r-release: CausalModels_0.1.0.zip, r-oldrel: CausalModels_0.2.0.zip
macOS binaries: r-release (arm64): CausalModels_0.1.0.tgz, r-oldrel (arm64): CausalModels_0.1.0.tgz, r-release (x86_64): CausalModels_0.2.0.tgz, r-oldrel (x86_64): CausalModels_0.2.0.tgz
Old sources: CausalModels archive

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