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Cite Stan packages in a project/files

Source: R/citation.R
stan_cite.Rd

stan_cite() finds Stan packages and functions used in a project, then returns their citations as BibTeX or bibentry records.

Usage

stan_cite(
  path = ".",
  strict = FALSE,
  format = c("bibtex", "bibentry"),
  skip_dirs = ascribe::scan_skip_dirs(),
  ignore_unqualified_functions = ascribe::stdlib_funs(),
  use_knitr = FALSE,
  quiet = getOption("stanflow.quiet", FALSE)
)

Arguments

path

A single project directory (searched recursively) or a vector of files (.R/.Rmd/.qmd).

strict

If FALSE (default), warn on ambiguous function calls whose origin cannot be determined exactly. If TRUE, abort on ambiguous calls.

format

One of "bibtex" or "bibentry".

skip_dirs

Character vector of directory names to skip when scanning a directory. Defaults to scan_skip_dirs().

ignore_unqualified_functions

Defaults to exports from base R packages listed in stdlib_funs(). Character vector of function names to ignore when attributing (unqualified) calls. Calls like pkg::fun() will NOT be ignored even if fun is in ignore_unqualified_functions, since they are namespaced.

use_knitr

Logical. If TRUE, parse .Rmd and .qmd files with knitr::purl(), which resolves knitr features the in-house parser ignores, such as child documents. It also comments out eval=FALSE and purl=FALSE chunks, so usage in them goes unrecorded. Defaults to FALSE.

quiet

Logical. If TRUE, suppresses status messages. Defaults to FALSE.

Value

A BibTeX character vector or a bibentry object.

Examples

path <- tempfile(fileext = ".R")
writeLines(
  c(
    "# one messy analysis file",
    "library(posterior)",
    "requireNamespace(\"loo\")",
    "draws <- as_draws(list(mu = rnorm(10)))",
    "posterior::rhat(draws)",
    "loo::loo(matrix(1))"
  ),
  path
)

stan_cite(path, quiet = TRUE)
#> @Manual{loo,
#>   title = {Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
#> Models},
#>   author = {Aki Vehtari and Jonah Gabry and Måns Magnusson and Yuling Yao and Paul-Christian Bürkner and Topi Paananen and Andrew Gelman},
#>   year = {2026},
#>   note = {R package version 2.10.1, https://discourse.mc-stan.org},
#>   url = {https://mc-stan.org/loo/},
#> }
#> 
#> @Manual{posterior,
#>   title = {Tools for Working with Posterior Distributions},
#>   author = {Paul-Christian Bürkner and Jonah Gabry and Matthew Kay and Aki Vehtari},
#>   year = {2026},
#>   note = {R package version 1.7.0, https://discourse.mc-stan.org},
#>   url = {https://mc-stan.org/posterior/},
#> }
#> 
#> @Article{burkner-2026-posterior,
#>   title = {posterior: Tools for Working with Posterior Distributions in R},
#>   author = {Paul-Christian B\u00fcrkner and Jonah Gabry and Matthew Kay and Aki Vehtari},
#>   journal = {Journal of Open Source Software},
#>   year = {2026},
#>   volume = {11},
#>   number = {122},
#>   pages = {10526},
#>   doi = {10.21105/joss.10526},
#>   url = {https://doi.org/10.21105/joss.10526},
#>   publisher = {The Open Journal},
#>   encoding = {UTF-8},
#> }
#> 
#> @Article{vehtari-2021-rhat,
#>   title = {Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC (with discussion)},
#>   author = {Aki Vehtari and Andrew Gelman and Daniel Simpson and Bob Carpenter and Paul-Christian B\u00fcrkner},
#>   journal = {Bayesian Analysis},
#>   year = {2021},
#>   volume = {16},
#>   number = {2},
#>   pages = {667--718},
#>   doi = {10.1214/20-BA1221},
#> }
#> 
#> @Manual{stanflow,
#>   title = {A Mildly Opinionated Stan Bayesian Workflow},
#>   author = {Visruth {Srimath Kandali}},
#>   year = {2026},
#>   note = {R package version 0.2.0, https://discourse.mc-stan.org},
#>   url = {https://mc-stan.org/stanflow/},
#> }
#> 
#> @Manual{,
#>   title = {R: A Language and Environment for Statistical Computing},
#>   author = {{R Core Team}},
#>   organization = {R Foundation for Statistical Computing},
#>   address = {Vienna, Austria},
#>   year = {2026},
#>   doi = {10.32614/R.manuals},
#>   url = {https://www.R-project.org/},
#> }
#> 
#> @Article{vehtari-2017-loo,
#>   title = {Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC},
#>   author = {Aki Vehtari and Andrew Gelman and Jonah Gabry},
#>   journal = {Statistics and Computing},
#>   year = {2017},
#>   volume = {27},
#>   number = {5},
#>   pages = {1413--1432},
#>   doi = {10.1007/s11222-016-9696-4},
#>   note = {arXiv preprint: https://arxiv.org/abs/1507.04544},
#> }
#> 
#> @Article{vehtari-2024-psis,
#>   title = {Pareto smoothed importance sampling},
#>   author = {Aki Vehtari and Daniel Simpson and Andrew Gelman and Yuling Yao and Jonah Gabry},
#>   journal = {Journal of Machine Learning Research},
#>   year = {2024},
#>   volume = {25},
#>   number = {72},
#>   pages = {1--58},
#>   url = {https://jmlr.org/papers/v25/19-556.html},
#> }
stan_cite(path, format = "bibentry", quiet = TRUE)
#> Vehtari A, Gabry J, Magnusson M, Yao Y, Bürkner P, Paananen T, Gelman A
#> (2026). _Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
#> Models_. R package version 2.10.1, https://discourse.mc-stan.org,
#> <https://mc-stan.org/loo/>.
#> 
#> Bürkner P, Gabry J, Kay M, Vehtari A (2026). _Tools for Working with
#> Posterior Distributions_. R package version 1.7.0,
#> https://discourse.mc-stan.org, <https://mc-stan.org/posterior/>.
#> 
#> B\u00fcrkner P, Gabry J, Kay M, Vehtari A (2026). “posterior: Tools for
#> Working with Posterior Distributions in R.” _Journal of Open Source
#> Software_, *11*(122), 10526. doi:10.21105/joss.10526
#> <https://doi.org/10.21105/joss.10526>.
#> <https://doi.org/10.21105/joss.10526>.
#> 
#> Vehtari A, Gelman A, Simpson D, Carpenter B, B\u00fcrkner P (2021).
#> “Rank-normalization, folding, and localization: An improved R-hat for
#> assessing convergence of MCMC (with discussion).” _Bayesian Analysis_,
#> *16*(2), 667-718. doi:10.1214/20-BA1221
#> <https://doi.org/10.1214/20-BA1221>.
#> 
#> Srimath Kandali V (2026). _A Mildly Opinionated Stan Bayesian
#> Workflow_. R package version 0.2.0, https://discourse.mc-stan.org,
#> <https://mc-stan.org/stanflow/>.
#> 
#> R Core Team (2026). _R: A Language and Environment for Statistical
#> Computing_. R Foundation for Statistical Computing, Vienna, Austria.
#> doi:10.32614/R.manuals <https://doi.org/10.32614/R.manuals>.
#> <https://www.R-project.org/>.
#> 
#> Vehtari A, Gelman A, Gabry J (2017). “Practical Bayesian model
#> evaluation using leave-one-out cross-validation and WAIC.” _Statistics
#> and Computing_, *27*(5), 1413-1432. doi:10.1007/s11222-016-9696-4
#> <https://doi.org/10.1007/s11222-016-9696-4>. arXiv preprint:
#> https://arxiv.org/abs/1507.04544.
#> 
#> Vehtari A, Simpson D, Gelman A, Yao Y, Gabry J (2024). “Pareto smoothed
#> importance sampling.” _Journal of Machine Learning Research_, *25*(72),
#> 1-58. <https://jmlr.org/papers/v25/19-556.html>.
unlink(path)