Package: bayesmsm 1.0.0

Kuan Liu

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.

Authors:Kuan Liu [aut, cre, cph], Xiao Yan [aut], Martin Urner [aut]

bayesmsm_1.0.0.tar.gz
bayesmsm_1.0.0.zip(r-4.7-any)bayesmsm_1.0.0.zip(r-4.6-any)bayesmsm_1.0.0.zip(r-4.5-any)
bayesmsm_1.0.0.tgz(r-4.6-any)bayesmsm_1.0.0.tgz(r-4.5-any)
bayesmsm_1.0.0.tar.gz(r-4.7-any)bayesmsm_1.0.0.tar.gz(r-4.6-any)
bayesmsm_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bayesmsm/json (API)

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

Bug tracker:https://github.com/kuan-liu-lab/bayesmsm/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

bayesian-methodscausal-inferencemarginal-structural-modelsjagscpp

4.71 score 3 stars 17 scripts 340 downloads 8 exports 39 dependencies

Last updated from:69f5b11a8a. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK171
source / vignettesOK280
linux-releaseOK175
macos-releaseOK186
macos-oldrelOK117
windows-develOK125
windows-releaseOK122
windows-oldrelOK128
wasm-releaseOK102

Exports:bayesmsmbayesweightbayesweight_cenplot_APOplot_ATEplot_est_boxsimDatasummary_bayesmsm

Dependencies:abindbootclicodacodetoolscpp11doParallelfarverforeachggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixMatrixModelsmcmcMCMCpackquantregR2jagsR2WinBUGSR6RColorBrewerrjagsrlangS7scalesSparseMstringistringrsurvivalvctrsviridisLitewithr

bayesmsm for longitudinal data with informative right-censoring
Introduction | Simulated longitudinal observational data with right-censoring | Bayesian treatment effect weight estimation using bayesweight_cen | Bayesian non-parametric bootstrap to maximize the utility function with respect to the causal effect using bayesmsm | Visualization functions: plot_ATE, plot_APO, plot_est_box | Reference
Rendered frombayesmsm-censoring.Rmdusingknitr::rmarkdown

Last update: 2025-06-13
Started: 2024-10-21

bayesmsm for longitudinal data without right-censoring
Introduction | Simulated longitudinal observational data without right-censoring | Bayesian treatment effect weight estimation using bayesweight | Bayesian non-parametric bootstrap to maximize the utility function with respect to the causal effect using bayesmsm | Visualization functions: plot_ATE, plot_APO, plot_est_box | Reference
Rendered frombayesmsm-nocensoring.Rmdusingknitr::rmarkdown

Last update: 2025-06-13
Started: 2024-10-08