mvGPS: Causal Inference using Multivariate Generalized Propensity Score

Methods for estimating and utilizing the multivariate generalized propensity score (mvGPS) for multiple continuous exposures described in Williams, J.R, and Crespi, C.M. (2020) <arXiv:2008.13767>. The methods allow estimation of a dose-response surface relating the joint distribution of multiple continuous exposure variables to an outcome. Weights are constructed assuming a multivariate normal density for the marginal and conditional distribution of exposures given a set of confounders. Confounders can be different for different exposure variables. The weights are designed to achieve balance across all exposure dimensions and can be used to estimate dose-response surfaces.

Version: 1.2.1
Depends: R (≥ 3.6)
Imports: Rdpack, MASS, WeightIt, cobalt, matrixNormal, geometry, sp, gbm, CBPS
Suggests: testthat, knitr, dagitty, ggdag, dplyr, rmarkdown, ggplot2
Published: 2021-10-16
Author: Justin Williams ORCID iD [aut, cre]
Maintainer: Justin Williams <williazo at ucla.edu>
BugReports: https://github.com/williazo/mvGPS/issues
License: MIT + file LICENSE
URL: https://github.com/williazo/mvGPS
NeedsCompilation: no
Citation: mvGPS citation info
Materials: NEWS
CRAN checks: mvGPS results

Documentation:

Reference manual: mvGPS.pdf
Vignettes: mvGPS-intro

Downloads:

Package source: mvGPS_1.2.1.tar.gz
Windows binaries: r-devel: mvGPS_1.2.1.zip, r-release: mvGPS_1.2.1.zip, r-oldrel: mvGPS_1.2.1.zip
macOS binaries: r-release (arm64): mvGPS_1.1.1.tgz, r-release (x86_64): mvGPS_1.2.1.tgz, r-oldrel: mvGPS_1.2.1.tgz
Old sources: mvGPS archive

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