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]

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

On CRAN:

4 exports 12 stars 1.59 score 59 dependencies 1 scripts 269 downloads

Last updated 3 years agofrom:babf41bf51. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:balgen_Dhull_samplemvGPS

Dependencies:abindbackportsCBPSchkclicobaltcodetoolscolorspacecrayonfansifarverforeachgbmgenericsgeometryggplot2glmnetgluegridExtragtableisobanditeratorslabelinglatticelifecyclelinproglpSolvemagicmagrittrMASSMatchItMatrixmatrixNormalmgcvmunsellmvtnormnlmennetnumDerivpillarpkgconfigR6rbibutilsRColorBrewerRcppRcppEigenRcppProgressRdpackrlangscalesshapespsurvivaltibbleutf8vctrsviridisLiteWeightItwithr