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CausalModels
View on CRAN: Click
here
Download and install CausalModels package within the R console
Install from CRAN:
install.packages("CausalModels")
Install from Github:
library("remotes")
install_github("cran/CausalModels")
Install by package version:
library("remotes")
install_version("CausalModels", "0.2.0")
Attach the package and use:
library("CausalModels")
Maintained by
Joshua Anderson
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-05-30
Latest Update: 2022-11-23
Description:
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) ).
How to cite:
Joshua Anderson (2022). CausalModels: Causal Inference Modeling for Estimation of Causal Effects. R package version 0.2.0, https://cran.r-project.org/web/packages/CausalModels. Accessed 30 Jan. 2025.
Previous versions and publish date:
0.1.0 (2022-05-30 17:40)
Other packages that cited CausalModels R package
View CausalModels citation profile
Other R packages that CausalModels depends,
imports, suggests or enhances
Complete documentation for CausalModels
Functions, R codes and Examples using
the CausalModels R package
Some associated functions: doubly_robust . gestimation . init_params . ipweighting . iv_est . outcome_regression . propensity_matching . propensity_scores . standardization .
Some associated R codes: doubly_robust.R . gestimation.R . helper_funcs.R . imports.R . initialize.R . ipweighting.R . iv_est.R . outcome_regression.R . propensity_matching.R . propensity_scores.R . standardization.R . zzz.R . Full CausalModels package functions and examples
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