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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 |
| Reference manual: | CausalModels.pdf |
| Package source: | CausalModels_0.2.0.tar.gz |
| Windows binaries: | r-devel: CausalModels_0.2.0.zip, r-release: CausalModels_0.2.0.zip, r-oldrel: CausalModels_0.2.0.zip |
| macOS binaries: | r-release (arm64): CausalModels_0.2.0.tgz, r-oldrel (arm64): CausalModels_0.2.0.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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These binaries (installable software) and packages are in development.
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