WeightIt: Weighting for Covariate Balance in Observational Studies
Generates balancing weights for causal effect estimation in observational studies with
binary, multi-category, or continuous point or longitudinal treatments by easing and
extending the functionality of several R packages and providing in-house estimation methods.
Available methods include propensity score weighting using generalized linear models, gradient
boosting machines, the covariate balancing propensity score algorithm, Bayesian additive regression trees, and
SuperLearner, and directly estimating balancing weights using entropy balancing, empirical
balancing calibration weights, energy balancing, and optimization-based weights. Also
allows for assessment of weights and checking of covariate balance by interfacing directly
with the 'cobalt' package. See the vignette "Installing Supporting Packages" for instructions on how
to install any package 'WeightIt' uses, including those that may not be on CRAN.
| Version: |
0.13.1 |
| Depends: |
R (≥ 3.3.0) |
| Imports: |
cobalt (≥ 4.3.0), ggplot2 (≥ 3.3.0), crayon, backports (≥
1.4.1) |
| Suggests: |
twang (≥ 1.5), CBPS (≥ 0.18), ATE (≥ 0.2.0), optweight (≥
0.2.4), SuperLearner (≥ 2.0-25), mlogit (≥ 1.1.0), mclogit, MNP (≥ 3.1-0), brglm2 (≥ 0.5.2), osqp (≥ 0.6.0.5), survey, boot, MASS, gbm (≥ 2.1.3), dbarts (≥ 0.9-20), misaem (≥
1.0.1), knitr, rmarkdown |
| Published: |
2022-06-28 |
| Author: |
Noah Greifer 
[aut, cre] |
| Maintainer: |
Noah Greifer <noah.greifer at gmail.com> |
| BugReports: |
https://github.com/ngreifer/WeightIt/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://ngreifer.github.io/WeightIt/,
https://github.com/ngreifer/WeightIt |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| In views: |
CausalInference |
| CRAN checks: |
WeightIt results |
Documentation:
Downloads:
Reverse dependencies:
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