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) <doi:10.48550/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: 1.2.2
Depends: R (≥ 3.6)
Imports: Rdpack, MASS, WeightIt, cobalt, matrixNormal, geometry, sp, gbm, CBPS
Suggests: testthat, knitr, dagitty, ggdag, dplyr, rmarkdown, ggplot2
Published: 2021-12-07
DOI: 10.32614/CRAN.package.mvGPS
Author: Justin Williams ORCID iD [aut, cre]
Maintainer: Justin Williams <williazo at>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: mvGPS citation info
Materials: NEWS
In views: CausalInference
CRAN checks: mvGPS results


Reference manual: mvGPS.pdf
Vignettes: mvGPS-intro


Package source: mvGPS_1.2.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mvGPS_1.2.2.tgz, r-oldrel (arm64): mvGPS_1.2.2.tgz, r-release (x86_64): mvGPS_1.2.2.tgz, r-oldrel (x86_64): mvGPS_1.2.2.tgz
Old sources: mvGPS archive


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