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: 1.1.1
Depends: R (≥ 3.6)
Imports: Rdpack, MASS, WeightIt, cobalt, matrixNormal, geometry, sp, gbm, CBPS
Suggests: testthat, knitr, dagitty, ggdag, dplyr, rmarkdown
Published: 2021-04-28
Author: Justin Williams ORCID iD [aut, cre]
Maintainer: Justin Williams <williazo at>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: mvGPS citation info
Materials: NEWS
CRAN checks: mvGPS results


Reference manual: mvGPS.pdf
Vignettes: mvGPS-intro
Package source: mvGPS_1.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: mvGPS_1.1.1.tgz, r-oldrel: mvGPS_1.1.1.tgz
Old sources: mvGPS archive


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