Package: CausalModels 0.2.1

Joshua Anderson

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://miguelhernan.org/whatifbook/>).

Authors:Joshua Anderson [aut, cre, cph], Cyril Rakovski [rev], Yesha Patel [rev], Erin Lee [rev]

CausalModels_0.2.1.tar.gz
CausalModels_0.2.1.zip(r-4.7)CausalModels_0.2.1.zip(r-4.6)CausalModels_0.2.1.zip(r-4.5)
CausalModels_0.2.1.tgz(r-4.6-any)CausalModels_0.2.1.tgz(r-4.5-any)
CausalModels_0.2.1.tar.gz(r-4.7-any)CausalModels_0.2.1.tar.gz(r-4.6-any)
CausalModels_0.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
CausalModels/json (API)
NEWS

# Install 'CausalModels' in R:
install.packages('CausalModels', repos = c('https://ander428.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ander428/causalmodels/issues

On CRAN:

Conda:

3.78 score 12 stars 7 scripts 273 downloads 9 exports 35 dependencies

Last updated from:4a81259939. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK133
source / vignettesOK159
linux-release-x86_64OK163
macos-release-arm64OK195
macos-oldrel-arm64OK156
windows-develOK185
windows-releaseOK93
windows-oldrelOK113
wasm-releaseOK95

Exports:doubly_robustgestimationinit_paramsipweightingiv_estoutcome_regressionpropensity_matchingpropensity_scoresstandardization

Dependencies:backportsbootbroomcausaldataclicodetoolscpp11dplyrgeepackgenericsgluelatticelifecyclemagrittrMASSMatrixmultcompmvtnormpillarpkgconfigpurrrR6rlangsandwichstringistringrsurvivalTH.datatibbletidyrtidyselectutf8vctrswithrzoo