jointVIP: Prioritize Variables with Joint Variable Importance Plot in Observational Study Design (original) (raw)

In the observational study design stage, matching/weighting methods are conducted. However, when many background variables are present, the decision as to which variables to prioritize for matching/weighting is not trivial. Thus, the joint treatment-outcome variable importance plots are created to guide variable selection. The joint variable importance plots enhance variable comparisons via unadjusted bias curves derived under the omitted variable bias framework. The plots translate variable importance into recommended values for tuning parameters in existing methods. Post-matching and/or weighting plots can also be used to visualize and assess the quality of the observational study design. The method motivation and derivation is presented in "Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot" by Liao et al. (2024) <doi:10.1080/00031305.2024.2303419>. See the package paper by Liao and Pimentel (2024) <doi:10.21105/joss.06093> for a beginner friendly user introduction.

Version:	1.0.1
Depends:	R (≥ 3.3)
Imports:	ggrepel (≥ 0.9.2), ggplot2 (≥ 3.4.0)
Suggests:	causaldata, devtools (≥ 2.4.5), knitr, MatchIt, WeightIt, optmatch, optweight (≥ 0.2.4), rmarkdown (≥ 2.18), testthat (≥ 3.0.0), stringr
Published:	2025-09-12
DOI:	10.32614/CRAN.package.jointVIP
Author:	Lauren D. Liao [aut, cre], Samuel D. Pimentel [aut]
Maintainer:	Lauren D. Liao
BugReports:	https://github.com/ldliao/jointVIP/issues
License:	MIT + file
URL:	https://github.com/ldliao/jointVIP,https://ldliao.github.io/jointVIP/
NeedsCompilation:	no
Citation:	jointVIP citation info
Materials:	README, NEWS
CRAN checks:	jointVIP results

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