doi:10.1111/biom.12269> for methodological details.">

bayesmsm: Fitting Bayesian Marginal Structural Models for Longitudinal Observational Data (original) (raw)

Implements Bayesian marginal structural models for causal effect estimation with time-varying treatment and confounding. It includes an extension to handle informative right censoring. The Bayesian importance sampling weights are estimated using JAGS. See Saarela (2015) <doi:10.1111/biom.12269> for methodological details.

Version: 1.0.0
Depends: R (≥ 4.2.0)
Imports: coda (≥ 0.19-4), doParallel, foreach, ggplot2, graphics, grDevices, MCMCpack, parallel, R2jags, stats
Suggests: devtools, knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2025-06-17
DOI: 10.32614/CRAN.package.bayesmsm
Author: Kuan Liu ORCID iD [aut, cre, cph], Xiao Yan ORCID iD [aut], Martin Urner ORCID iD [aut]
Maintainer: Kuan Liu <kuan.liu at utoronto.ca>
BugReports: https://github.com/Kuan-Liu-Lab/bayesmsm/issues
License: MIT + file
URL: https://github.com/Kuan-Liu-Lab/bayesmsm
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: bayesmsm results

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