Package: mvGPS
Title: Causal Inference using Multivariate Generalized Propensity Score
Version: 1.2.2
Authors@R: 
    person(given = "Justin",
           family = "Williams",
           role = c("aut", "cre"),
           email = "williazo@ucla.edu",
           comment = c(ORCID = "https://orcid.org/0000-0002-5045-2764"))
Description: 
    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.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: false
RoxygenNote: 7.1.0
RdMacros: Rdpack
VignetteBuilder: knitr
Depends: R (>= 3.6)
Imports: Rdpack, MASS, WeightIt, cobalt, matrixNormal, geometry, sp,
        gbm, CBPS
BugReports: https://github.com/williazo/mvGPS/issues
URL: https://github.com/williazo/mvGPS
Suggests: testthat, knitr, dagitty, ggdag, dplyr, rmarkdown, ggplot2
NeedsCompilation: no
Packaged: 2021-12-06 19:52:49 UTC; williazo
Author: Justin Williams [aut, cre] (<https://orcid.org/0000-0002-5045-2764>)
Maintainer: Justin Williams <williazo@ucla.edu>
Repository: CRAN
Date/Publication: 2021-12-07 08:20:15 UTC
Built: R 4.6.0; ; 2026-04-26 09:01:49 UTC; unix
