mvGPS: Causal Inference using Multivariate Generalized Propensity Score

Methods for estimating weights and generalized propensity score for multiple continuous exposures via the generalized propensity score described in Williams, J.R, and Cresi, C.M (2020) <arXiv:2008.13767>. Weights are constructed assuming an underlying multivariate normal density for the marginal and conditional distribution of exposures given a set of confounders. These weights can then be used to estimate dose-response curves or surfaces. This method achieves balance across all exposure dimension rather than along a single dimension.

Version: 1.0.2
Depends: R (≥ 3.6)
Imports: Rdpack, MASS, WeightIt, cobalt, matrixNormal, geometry, sp, gbm, CBPS
Suggests: testthat, knitr, dagitty, ggdag, dplyr, rmarkdown
Published: 2020-09-17
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

Downloads:

Reference manual: mvGPS.pdf
Vignettes: mvGPS-intro
Package source: mvGPS_1.0.2.tar.gz
Windows binaries: r-devel: mvGPS_1.0.2.zip, r-release: mvGPS_1.0.2.zip, r-oldrel: mvGPS_1.0.2.zip
macOS binaries: r-release: mvGPS_1.0.2.tgz, r-oldrel: mvGPS_1.0.2.tgz

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