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 [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 In views: CausalInference CRAN checks: mvGPS results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=mvGPS to link to this page.
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