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 [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_1.1.1.tar.gz
Windows binaries:	r-devel: mvGPS_1.1.1.zip, r-release: mvGPS_1.1.1.zip, r-oldrel: mvGPS_1.1.1.zip
macOS binaries:	r-release (arm64): mvGPS_1.1.1.tgz, r-release (x86_64): mvGPS_1.1.1.tgz, r-oldrel: mvGPS_1.1.1.tgz
Old sources:	mvGPS archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=mvGPS to link to this page.