bayesmsm: Fitting Bayesian Marginal Structural Models for Longitudinal Observational Data

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 [aut, cre, cph], Xiao Yan [aut], Martin Urner [aut]
Maintainer:	Kuan Liu <kuan.liu at utoronto.ca>
BugReports:	https://github.com/Kuan-Liu-Lab/bayesmsm/issues
License:	MIT + file LICENSE
URL:	https://github.com/Kuan-Liu-Lab/bayesmsm
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	bayesmsm results

Documentation:

Reference manual:	bayesmsm.pdf
Vignettes:	'bayesmsm' for longitudinal data with informative right-censoring (source, R code) 'bayesmsm' for longitudinal data without right-censoring (source, R code)

Downloads:

Package source:	bayesmsm_1.0.0.tar.gz
Windows binaries:	r-devel: not available, r-release: bayesmsm_1.0.0.zip, r-oldrel: bayesmsm_1.0.0.zip
macOS binaries:	r-release (arm64): bayesmsm_1.0.0.tgz, r-oldrel (arm64): bayesmsm_1.0.0.tgz, r-release (x86_64): not available, r-oldrel (x86_64): not available

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