jointVIP: Prioritize Variables with Joint Variable Importance Plot in Observational Study Design

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 bias curves, derived using the classical omitted variable bias framework. The joint variable importance 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.

Version:	0.1.0
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)
Published:	2022-12-21
Author:	Lauren D. Liao [aut, cre], Samuel D. Pimentel [aut]
Maintainer:	Lauren D. Liao <ldliao at berkeley.edu>
BugReports:	https://github.com/ldliao/jointVIP/issues
License:	MIT + file LICENSE
URL:	https://github.com/ldliao/jointVIP
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	jointVIP results

Documentation:

Reference manual:	jointVIP.pdf
Vignettes:	additional_options Get started with jointVIP

Downloads:

Package source:	jointVIP_0.1.0.tar.gz
Windows binaries:	r-devel: jointVIP_0.1.0.zip, r-release: jointVIP_0.1.0.zip, r-oldrel: jointVIP_0.1.0.zip
macOS binaries:	r-release (arm64): jointVIP_0.1.0.tgz, r-oldrel (arm64): jointVIP_0.1.0.tgz, r-release (x86_64): jointVIP_0.1.0.tgz, r-oldrel (x86_64): jointVIP_0.1.0.tgz

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