doi:10.1093/jrsssa/qnae012> and Hartman and Huang (2024) <doi:10.1017/pan.2023.12>. The package allows researchers to generate the set of recommended sensitivity summaries to evaluate the sensitivity in their underlying weighting estimators to omitted moderators or confounders. The tools can be flexibly applied in causal inference settings (i.e., in external and internal validity contexts) or survey contexts.">

senseweight: Sensitivity Analysis for Weighted Estimators (original) (raw)

Provides tools to conduct interpretable sensitivity analyses for weighted estimators, introduced in Huang (2024) <doi:10.1093/jrsssa/qnae012> and Hartman and Huang (2024) <doi:10.1017/pan.2023.12>. The package allows researchers to generate the set of recommended sensitivity summaries to evaluate the sensitivity in their underlying weighting estimators to omitted moderators or confounders. The tools can be flexibly applied in causal inference settings (i.e., in external and internal validity contexts) or survey contexts.

Version: 0.0.1
Depends: R (≥ 4.1.0)
Imports: dplyr, estimatr, ggplot2, ggrepel, kableExtra, metR, rlang, survey, WeightIt
Suggests: knitr, pkgload, rmarkdown
Published: 2025-08-22
DOI: 10.32614/CRAN.package.senseweight
Author: Melody Huang [aut, cre]
Maintainer: Melody Huang <melody.huang at yale.edu>
License: MIT + file
URL: https://melodyyhuang.github.io/senseweight/
NeedsCompilation: no
Materials: README
CRAN checks: senseweight results

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