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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "mvGPS" in publications use:'
type: software
license: MIT
title: 'mvGPS: Causal Inference using Multivariate Generalized Propensity Score'
version: 1.2.2
identifiers:
- type: doi
  value: 10.32614/CRAN.package.mvGPS
abstract: 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.
authors:
- family-names: Williams
  given-names: Justin
  email: williazo@ucla.edu
  orcid: https://orcid.org/0000-0002-5045-2764
preferred-citation:
  type: generic
  title: Causal inference for multiple continuous exposures via the multivariate generalized
    propensity score
  authors:
  - family-names: Williams
    given-names: Justin R.
  - family-names: Crespi
    given-names: Catherine M.
  year: '2020'
  notes: arXiv:2008.13767
repository: https://williazo.r-universe.dev
repository-code: https://github.com/williazo/mvGPS
commit: babf41bf51672b722e5b987a83d624ffcc6d91c8
url: https://github.com/williazo/mvGPS
contact:
- family-names: Williams
  given-names: Justin
  email: williazo@ucla.edu
  orcid: https://orcid.org/0000-0002-5045-2764