Package: stantargets 0.1.1.9000

William Michael Landau

stantargets: Targets for Stan Workflows

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the 'stantargets' R package leverages 'targets' and 'cmdstanr' to ease these burdens. 'stantargets' makes it super easy to set up scalable Stan 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. 'stantargets' can access all of 'cmdstanr''s major algorithms (MCMC, variational Bayes, and optimization) and it supports both single-fit workflows and multi-rep simulation studies. For the statistical methodology, please refer to 'Stan' documentation (Stan Development Team 2020) <https://mc-stan.org/>.

Authors:William Michael Landau [aut, cre], Krzysztof Sakrejda [rev], Matthew T. Warkentin [rev], Eli Lilly and Company [cph]

stantargets_0.1.1.9000.tar.gz
stantargets_0.1.1.9000.zip(r-4.5)stantargets_0.1.1.9000.zip(r-4.4)stantargets_0.1.1.9000.zip(r-4.3)
stantargets_0.1.1.9000.tgz(r-4.4-any)stantargets_0.1.1.9000.tgz(r-4.3-any)
stantargets_0.1.1.9000.tar.gz(r-4.5-noble)stantargets_0.1.1.9000.tar.gz(r-4.4-noble)
stantargets_0.1.1.9000.tgz(r-4.4-emscripten)stantargets_0.1.1.9000.tgz(r-4.3-emscripten)
stantargets.pdf |stantargets.html
stantargets/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/ropensci/stantargets/issues

On CRAN:

bayesianhigh-performance-computingmaker-targetopiareproducibilitystanstatisticstargets

29 exports 47 stars 3.03 score 54 dependencies

Last updated 3 months agofrom:9ffea0b03b8f8a6774b6e52d5bc8b181dcffe37b (via main)

Exports:tar_stan_compiletar_stan_compile_runtar_stan_example_datatar_stan_example_filetar_stan_gqtar_stan_gq_rep_drawstar_stan_gq_rep_runtar_stan_gq_rep_summarytar_stan_gq_runtar_stan_mcmctar_stan_mcmc_rep_diagnosticstar_stan_mcmc_rep_drawstar_stan_mcmc_rep_runtar_stan_mcmc_rep_summarytar_stan_mcmc_runtar_stan_mletar_stan_mle_rep_drawstar_stan_mle_rep_runtar_stan_mle_rep_summarytar_stan_mle_runtar_stan_outputtar_stan_rep_data_batchtar_stan_summarytar_stan_summary_join_datatar_stan_vbtar_stan_vb_rep_drawstar_stan_vb_rep_runtar_stan_vb_rep_summarytar_stan_vb_run

Dependencies:abindbackportsbase64urlBHcallrcheckmateclicmdstanrcodetoolscpp11data.tabledistributionaldplyrevaluatefansifsfstfstcoregenericsgluehighrigraphjsonliteknitrlatticelifecyclemagrittrMatrixmatrixStatsnumDerivpillarpkgconfigposteriorprocessxpspurrrqsR6RApiSerializeRcppRcppParallelrlangsecretbasestringfishtarchetypestargetstensorAtibbletidyselectutf8vctrswithrxfunyaml

Bayesian simulation pipelines with stantargets

Rendered fromsimulation.Rmdusingknitr::rmarkdownon Jun 04 2024.

Last update: 2024-04-05
Started: 2021-05-22

Introduction to stantargets

Rendered fromintroduction.Rmdusingknitr::rmarkdownon Jun 04 2024.

Last update: 2023-10-04
Started: 2021-05-22