jagstargets

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#48 of 48

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Score

4.7

The facts below are what the maker publishes about jagstargets, checked and dated. Our editors have not written about it yet.

The facts

Each one read from jagstargets's own pages, with the date it was checked.

Analysis outputs: The workflow can create posterior draws, summary statistics and convergence diagnostics, and deviance information criterion results.checked 4 Oct 2026
Distribution and license: The documentation offers installation from CRAN or the GitHub development version, and identifies the license as MIT.checked 4 Oct 2026
Efficiency: Pipelines can skip expensive steps when their results are already up to date and automatically parallelize computation.checked 4 Oct 2026
Integrations: jagstargets works with JAGS, R2jags, targets, and posterior.checked 4 Oct 2026
Intended users: The documentation says users should be familiar with targets, Bayesian statistics, and JAGS; prior experience with rjags or R2jags helps.checked 4 Oct 2026
Multiple models: You can supply multiple JAGS model files, which run on the same dataset.checked 4 Oct 2026
Purpose: jagstargets creates reproducible pipelines for Bayesian data analysis with JAGS, designed for Bayesian statisticians.checked 4 Oct 2026
Requirements: Users must install JAGS, rjags, and R2jags before installing jagstargets.checked 4 Oct 2026
Support: Users having trouble can ask for help in the project's GitHub discussions forum.checked 4 Oct 2026
Workflow: Its tar_jags() function declares targets for fitting JAGS models and managing their workflow through targets.checked 4 Oct 2026