Imports: data.table, parallel, runner, stats

Title: Causal Inference using Multivariate Generalized Propensity Score
Description: 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.
Imports: Rdpack, MASS, WeightIt, cobalt, matrixNormal, geometry, sp,
Suggests: testthat, knitr, dagitty, ggdag, dplyr, rmarkdown
Author: Justin Williams [aut, cre] (<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.

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.
Suggests: testthat, knitr, dagitty, ggdag, dplyr, rmarkdown, ggplot2