Package: mvGPS 1.2.2

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.

Authors:Justin Williams [aut, cre]

mvGPS_1.2.2.tar.gz
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mvGPS_1.2.2.tar.gz(r-4.5-noble)mvGPS_1.2.2.tar.gz(r-4.4-noble)
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mvGPS.pdf |mvGPS.html
mvGPS/json (API)
NEWS

# Install 'mvGPS' in R:
install.packages('mvGPS', repos = c('https://williazo.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/williazo/mvgps/issues

On CRAN:

4.81 score 13 stars 1 scripts 258 downloads 4 exports 59 dependencies

Last updated 3 years agofrom:babf41bf51. Checks:1 OK, 7 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 01 2025
R-4.5-winNOTEFeb 01 2025
R-4.5-macNOTEFeb 01 2025
R-4.5-linuxNOTEFeb 01 2025
R-4.4-winNOTEFeb 01 2025
R-4.4-macNOTEFeb 01 2025
R-4.3-winNOTEFeb 01 2025
R-4.3-macNOTEFeb 01 2025

Exports:balgen_Dhull_samplemvGPS

Dependencies:abindbackportsCBPSchkclicobaltcodetoolscolorspacecrayonfansifarverforeachgbmgenericsgeometryggplot2glmnetgluegridExtragtableisobanditeratorslabelinglatticelifecyclelinproglpSolvemagicmagrittrMASSMatchItMatrixmatrixNormalmgcvmunsellmvtnormnlmennetnumDerivpillarpkgconfigR6rbibutilsRColorBrewerRcppRcppEigenRcppProgressRdpackrlangscalesshapespsurvivaltibbleutf8vctrsviridisLiteWeightItwithr

Multivariate generalized propensity score: An introduction

Rendered frommvGPS-intro.Rmdusingknitr::rmarkdownon Feb 01 2025.

Last update: 2021-12-08
Started: 2020-09-01