Targets for JAGS Workflows

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the 'jagstargets' R package is leverages 'targets' and 'R2jags' to ease this burden. 'jagstargets' makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than 'targets' alone. For the underlying methodology, please refer to the documentation of 'targets' and 'JAGS' (Plummer 2003) < https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf>.


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Reference manual

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install.packages("jagstargets")

1.0.1 by William Michael Landau, 2 months ago


https://docs.ropensci.org/jagstargets/, https://github.com/ropensci/jagstargets


Report a bug at https://github.com/ropensci/jagstargets/issues


Browse source code at https://github.com/cran/jagstargets


Authors: William Michael Landau [aut, cre] , David Lawrence Miller [rev] , Eli Lilly and Company [cph]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports coda, digest, fst, posterior, purrr, qs, R2jags, rjags, rlang, stats, targets, tarchetypes, tibble, tools, utils, withr

Suggests dplyr, fs, knitr, R.utils, rmarkdown, testthat, tidyr, visNetwork

System requirements: JAGS 4.x.y (https://mcmc-jags.sourceforge.net)


See at CRAN