From 9365bfeca0604cacf173a806f06a8e300c4100b9 Mon Sep 17 00:00:00 2001 From: Aki Vehtari Date: Tue, 6 Oct 2026 21:45:25 +0300 Subject: [PATCH 1/3] add and fix citations --- R/mcmc-traces.R | 2 +- R/ppc-calibration.R | 7 +-- R/ppc-discrete.R | 2 + R/ppc-distributions.R | 8 +-- R/ppc-loo.R | 5 +- R/ppd-distributions.R | 1 + README.md | 44 +++++++++++++++++ inst/CITATION | 49 ++++++++++++++++++- man-roxygen/reference-uniformity-test.R | 9 ++-- man/MCMC-traces.Rd | 10 ++-- man/PPC-calibration.Rd | 7 +-- man/PPC-discrete.Rd | 5 ++ man/PPC-distributions.Rd | 23 ++++++--- man/PPC-loo.Rd | 13 +++-- man/PPD-distributions.Rd | 6 +++ .../loo-pit-correlated-tests.Rmd | 8 +-- .../articles-online-only/ppc-calibration.Rmd | 11 +++-- 17 files changed, 166 insertions(+), 44 deletions(-) diff --git a/R/mcmc-traces.R b/R/mcmc-traces.R index 699c2fe4b..4ef9e4aec 100644 --- a/R/mcmc-traces.R +++ b/R/mcmc-traces.R @@ -72,7 +72,7 @@ #' is, bands that completely cover all of the rank ECDFs with the probability #' `prob`. If `plot_diff = TRUE`, the difference between the observed rank #' ECDFs and the theoretical expectation for samples originating from the -#' same distribution is drawn. See Säilynoja et al. (2021) for details. +#' same distribution is drawn. See Säilynoja et al. (2022) for details. #' } #' \item{`mcmc_trace_data()`}{ #' Data-preparation back end for `mcmc_trace()`, `mcmc_trace_highlight()`, diff --git a/R/ppc-calibration.R b/R/ppc-calibration.R index f84291762..89a63b911 100644 --- a/R/ppc-calibration.R +++ b/R/ppc-calibration.R @@ -23,7 +23,7 @@ #' The PPC calibration functions are designed to assess the calibration of a #' model with binary outcomes. In this context, calibration refers to the #' agreement between predicted probabilities and conditional event probabilities -#' (CEPs) see Dimitriadis et al. (2021) and Säilynoja et al. (2025) for details. +#' (CEPs) see Dimitriadis et al. (2021) and Säilynoja et al. (2026) for details. #' #' The required inputs are `y`, representing binary observations #' (0 or 1), and either `yrep` or `prep`. Specifically, @@ -69,10 +69,7 @@ #' reliability diagrams for probabilistic classifiers. Proceedings of the #' National Academy of Sciences, 118(8). #' https://doi.org/10.1073/pnas.2016191118 -#' -#' Säilynoja, T., Johnson, A. R., Martin, O. A., & Vehtari, A. (2025). -#' Recommendations for visual predictive checks in Bayesian workflow. -#' (Preprint). arXiv. https://doi.org/10.48550/arXiv.2503.01509 +#' @template reference-vpc-recommendations #' #' @examples #' color_scheme_set("brightblue") diff --git a/R/ppc-discrete.R b/R/ppc-discrete.R index 743d4a13b..db8f9cc2c 100644 --- a/R/ppc-discrete.R +++ b/R/ppc-discrete.R @@ -102,6 +102,8 @@ #' These functions are not limited to discrete outcomes, but offer discrete-friendly #' displays for integer-valued statistics. #' +#' @template reference-vpc-recommendations +#' #' @examples #' set.seed(9222017) #' diff --git a/R/ppc-distributions.R b/R/ppc-distributions.R index 7dc43629a..796ef5c3b 100644 --- a/R/ppc-distributions.R +++ b/R/ppc-distributions.R @@ -59,7 +59,8 @@ #' simultaneous confidence intervals under an independence assumption. #' With `method = "correlated"`, the plot uses a dependence-aware #' uniformity assessment and can highlight suspicious regions. -#' See Säilynoja et al. (2025) and Tesso & Vehtari (2026) for details. +#' See Säilynoja et al. (2022), Säilynoja et al. (2026), and +#' Tesso & Vehtari (2026) for details. #' Note that the default "independent" method is **superseded** by #' the "correlated" method (Tesso & Vehtari, 2026) which accounts for #' dependent LOO-PIT values. @@ -76,6 +77,8 @@ #' #' @template reference-vis-paper #' @template reference-uniformity-test +#' @template reference-vpc-recommendations +#' @template reference-loo-pit-tests #' @templateVar bdaRef (Ch. 6) #' @template reference-bda #' @@ -703,9 +706,6 @@ ppc_violin_grouped <- #' If `NULL` (default), PIT values are computed internally. #' @param linewidth When `method = "correlated"`, the line width of the ECDF. #' Defaults to `0.3`. -#' -#' @references Tesso, H., & Vehtari, A. (2026). LOO-PIT predictive model -#' checking. arXiv preprint https://arxiv.org/abs/2603.02928. ppc_pit_ecdf <- function(y, yrep, ..., diff --git a/R/ppc-loo.R b/R/ppc-loo.R index b7e7ea9ea..e49f01138 100644 --- a/R/ppc-loo.R +++ b/R/ppc-loo.R @@ -88,6 +88,8 @@ #' @template reference-bda #' @template reference-vis-paper #' @template reference-loo +#' @template reference-uniformity-test +#' @template reference-loo-pit-tests #' #' @examples #' \dontrun{ @@ -424,9 +426,6 @@ ppc_loo_pit_qq <- function(y, #' the approximation gives a fast method for assessing the ECDF trajectory. #' For `ppc_loo_pit_ecdf()` when `method = 'independent'`. #' The default is to use interpolation if `K` is greater than 200. -#' -#' @references Tesso, H., & Vehtari, A. (2026). LOO-PIT predictive model -#' checking. arXiv preprint https://arxiv.org/abs/2603.02928. ppc_loo_pit_ecdf <- function(y, yrep, lw = NULL, diff --git a/R/ppd-distributions.R b/R/ppd-distributions.R index 51c738f04..55ce7475b 100644 --- a/R/ppd-distributions.R +++ b/R/ppd-distributions.R @@ -14,6 +14,7 @@ #' #' @template details-binomial #' @template return-ggplot-or-data +#' @template reference-vpc-recommendations #' #' @examples #' # difference between ppd_dens_overlay() and ppc_dens_overlay() diff --git a/README.md b/README.md index 42af1308c..c042247c2 100644 --- a/README.md +++ b/README.md @@ -47,6 +47,50 @@ _J. R. Stat. Soc. A_, 182: 389-402. doi:10.1111/rssa.12378. We are always looking for new contributors! See [CONTRIBUTING.md](https://raw.githubusercontent.com/stan-dev/bayesplot/master/.github/CONTRIBUTING.md) for details and/or reach out via the issue tracker. +### Citing bayesplot + +Developing and maintaining open source software is an important yet often +underappreciated contribution to scientific progress. Thus, whenever you are +using open source software (or software in general), please make sure to cite it +appropriately so that developers get credit for their work. + +When using **bayesplot**, please cite it as follows: + +* Gabry J, Mahr T (2026). bayesplot: Plotting for Bayesian Models. R package + version XXX, . + +* Gabry J, Simpson D, Vehtari A, Betancourt M, Gelman A (2019). Visualization + in Bayesian workflow. _J. R. Stat. Soc. A_, 182(2): 389-402. + doi:10.1111/rssa.12378. + +When using the graphical uniformity test in `mcmc_rank_ecdf()`, or in +`ppc_pit_ecdf()`, `ppc_pit_ecdf_grouped()`, and `ppc_loo_pit_ecdf()` with +`method = "independent"`, please also cite + +* Säilynoja T, Bürkner P-C, Vehtari A (2022). Graphical test for discrete + uniformity and its applications in goodness-of-fit evaluation and multiple + sample comparison. _Statistics and Computing_, 32(2): 32. + doi:10.1007/s11222-022-10090-6. + +When using the calibration plots (`ppc_calibration()`, +`ppc_calibration_overlay()`, `ppc_loo_calibration()`, and their `_grouped` +variants), the quantile dot plots `ppc_dots()` and `ppd_dots()`, +`ppc_rootogram()` with `style = "discrete"`, `ppc_rootogram_grouped()`, or the +`bounds` argument of the PPC and PPD density plots, please also cite + +* Säilynoja T, Johnson A, Martin O, Vehtari A (2026). Recommendations for + visual predictive checks in Bayesian workflow. _Journal of Visualization and + Interaction_, 1(1). doi:10.54337/jovi.v1i1.11478. + +When using the dependence-aware uniformity tests in `ppc_pit_ecdf()`, +`ppc_pit_ecdf_grouped()`, and `ppc_loo_pit_ecdf()` with +`method = "correlated"`, please also cite + +* Tesso H, Vehtari A (2026). LOO-PIT predictive model checking. _arXiv + preprint arXiv:2603.02928_. doi:10.48550/arXiv.2603.02928. + +The same information can be obtained by running `citation("bayesplot")`. + ### Installation * Install from CRAN: diff --git a/inst/CITATION b/inst/CITATION index 51bb98c6c..54fb1722d 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -22,8 +22,55 @@ bibentry(bibtype = "Article", year = "2019", journal = "J. R. Stat. Soc. A", volume = 182, - issue = 2, + number = 2, pages = "389-402", doi = "10.1111/rssa.12378", header = "To cite the bayesplot paper 'Visualization in Bayesian workflow':" ) + +bibentry(bibtype = "Article", + title = paste("Graphical test for discrete uniformity and its applications in", + "goodness-of-fit evaluation and multiple sample comparison"), + author = c(person("Teemu", "Säilynoja"), + person("Paul-Christian", "Bürkner"), + person("Aki", "Vehtari")), + year = "2022", + journal = "Statistics and Computing", + volume = 32, + number = 2, + pages = "32", + doi = "10.1007/s11222-022-10090-6", + header = "To cite the graphical uniformity test used in `mcmc_rank_ecdf()`, and in +`ppc_pit_ecdf()`, `ppc_pit_ecdf_grouped()`, and `ppc_loo_pit_ecdf()` with +`method = \"independent\"`:" +) + +bibentry(bibtype = "Article", + title = "Recommendations for visual predictive checks in Bayesian workflow", + author = c(person("Teemu", "Säilynoja"), + person("Andrew", "Johnson"), + person("Osvaldo", "Martin"), + person("Aki", "Vehtari")), + year = "2026", + journal = "Journal of Visualization and Interaction", + volume = 1, + number = 1, + doi = "10.54337/jovi.v1i1.11478", + header = "To cite the recommended visual predictive checks (`ppc_calibration()`, +`ppc_calibration_overlay()`, `ppc_loo_calibration()`, and their `_grouped` +variants, `ppc_dots()`, `ppd_dots()`, `ppc_rootogram()` with +`style = \"discrete\"`, `ppc_rootogram_grouped()`, and the `bounds` argument +of the PPC and PPD density plots):" +) + +bibentry(bibtype = "Article", + title = "LOO-PIT predictive model checking", + author = c(person("Herman", "Tesso"), + person("Aki", "Vehtari")), + year = "2026", + journal = "arXiv preprint arXiv:2603.02928", + doi = "10.48550/arXiv.2603.02928", + header = "To cite the dependence-aware uniformity tests (`ppc_pit_ecdf()`, +`ppc_pit_ecdf_grouped()`, and `ppc_loo_pit_ecdf()` with +`method = \"correlated\"`):" +) diff --git a/man-roxygen/reference-uniformity-test.R b/man-roxygen/reference-uniformity-test.R index c18e209a8..6fae4bc79 100644 --- a/man-roxygen/reference-uniformity-test.R +++ b/man-roxygen/reference-uniformity-test.R @@ -1,4 +1,5 @@ -#' @references Säilynoja, T., Bürkner, P., Vehtari, A. -#' (2021). Graphical Test for Discrete Uniformity and its Applications in -#' Goodness of Fit Evaluation and Multiple Sample Comparison [arXiv -#' preprint](https://arxiv.org/abs/2103.10522). +#' @references Säilynoja, T., Bürkner, P.-C., and Vehtari, A. (2022). +#' Graphical test for discrete uniformity and its applications in +#' goodness-of-fit evaluation and multiple sample comparison. +#' *Statistics and Computing*. 32(2), 32. doi:10.1007/s11222-022-10090-6. +#' arXiv preprint: diff --git a/man/MCMC-traces.Rd b/man/MCMC-traces.Rd index 2d884a053..5c2f19141 100644 --- a/man/MCMC-traces.Rd +++ b/man/MCMC-traces.Rd @@ -262,7 +262,7 @@ simultaneous confidence bands with a coverage determined by \code{prob}, that is, bands that completely cover all of the rank ECDFs with the probability \code{prob}. If \code{plot_diff = TRUE}, the difference between the observed rank ECDFs and the theoretical expectation for samples originating from the -same distribution is drawn. See Säilynoja et al. (2021) for details. +same distribution is drawn. See Säilynoja et al. (2022) for details. } \item{\code{mcmc_trace_data()}}{ Data-preparation back end for \code{mcmc_trace()}, \code{mcmc_trace_highlight()}, @@ -367,9 +367,11 @@ Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., Bürkner, P. (2019). Rank-normalization, folding, and localization: An improved \emph{R}-hat for assessing convergence of MCMC. \href{https://arxiv.org/abs/1903.08008}{arXiv preprint}. -Säilynoja, T., Bürkner, P., Vehtari, A. -(2021). Graphical Test for Discrete Uniformity and its Applications in -Goodness of Fit Evaluation and Multiple Sample Comparison \href{https://arxiv.org/abs/2103.10522}{arXiv preprint}. +Säilynoja, T., Bürkner, P.-C., and Vehtari, A. (2022). +Graphical test for discrete uniformity and its applications in +goodness-of-fit evaluation and multiple sample comparison. +\emph{Statistics and Computing}. 32(2), 32. doi:10.1007/s11222-022-10090-6. +arXiv preprint: \url{https://arxiv.org/abs/2103.10522} } \seealso{ Other MCMC: diff --git a/man/PPC-calibration.Rd b/man/PPC-calibration.Rd index ccfa0fd75..0dba1952c 100644 --- a/man/PPC-calibration.Rd +++ b/man/PPC-calibration.Rd @@ -192,7 +192,7 @@ details and examples. The PPC calibration functions are designed to assess the calibration of a model with binary outcomes. In this context, calibration refers to the agreement between predicted probabilities and conditional event probabilities -(CEPs) see Dimitriadis et al. (2021) and Säilynoja et al. (2025) for details. +(CEPs) see Dimitriadis et al. (2021) and Säilynoja et al. (2026) for details. The required inputs are \code{y}, representing binary observations (0 or 1), and either \code{yrep} or \code{prep}. Specifically, @@ -253,9 +253,10 @@ reliability diagrams for probabilistic classifiers. Proceedings of the National Academy of Sciences, 118(8). https://doi.org/10.1073/pnas.2016191118 -Säilynoja, T., Johnson, A. R., Martin, O. A., & Vehtari, A. (2025). +Säilynoja, T., Johnson, A., Martin, O., and Vehtari, A. (2026). Recommendations for visual predictive checks in Bayesian workflow. -(Preprint). arXiv. https://doi.org/10.48550/arXiv.2503.01509 +\emph{Journal of Visualization and Interaction}. 1(1). +doi:10.54337/jovi.v1i1.11478. } \seealso{ Other PPCs: diff --git a/man/PPC-discrete.Rd b/man/PPC-discrete.Rd index e46bfaf71..48d561f72 100644 --- a/man/PPC-discrete.Rd +++ b/man/PPC-discrete.Rd @@ -275,6 +275,11 @@ ppc_rootogram_grouped(y, yrep, group, style = "discrete", prob = 0.5) } \references{ +Säilynoja, T., Johnson, A., Martin, O., and Vehtari, A. (2026). +Recommendations for visual predictive checks in Bayesian workflow. +\emph{Journal of Visualization and Interaction}. 1(1). +doi:10.54337/jovi.v1i1.11478. + Kleiber, C. and Zeileis, A. (2016). Visualizing count data regressions using rootograms. \emph{The American Statistician}. 70(3): 296--303. diff --git a/man/PPC-distributions.Rd b/man/PPC-distributions.Rd index 1544b7cca..8d99e43d3 100644 --- a/man/PPC-distributions.Rd +++ b/man/PPC-distributions.Rd @@ -360,7 +360,8 @@ With \code{method = "independent"}, the plot shows \code{100 * prob}\% central simultaneous confidence intervals under an independence assumption. With \code{method = "correlated"}, the plot uses a dependence-aware uniformity assessment and can highlight suspicious regions. -See Säilynoja et al. (2025) and Tesso & Vehtari (2026) for details. +See Säilynoja et al. (2022), Säilynoja et al. (2026), and +Tesso & Vehtari (2026) for details. Note that the default "independent" method is \strong{superseded} by the "correlated" method (Tesso & Vehtari, 2026) which accounts for dependent LOO-PIT values. @@ -453,16 +454,24 @@ Gelman, A. (2019), Visualization in Bayesian workflow. \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) -Säilynoja, T., Bürkner, P., Vehtari, A. -(2021). Graphical Test for Discrete Uniformity and its Applications in -Goodness of Fit Evaluation and Multiple Sample Comparison \href{https://arxiv.org/abs/2103.10522}{arXiv preprint}. +Säilynoja, T., Bürkner, P.-C., and Vehtari, A. (2022). +Graphical test for discrete uniformity and its applications in +goodness-of-fit evaluation and multiple sample comparison. +\emph{Statistics and Computing}. 32(2), 32. doi:10.1007/s11222-022-10090-6. +arXiv preprint: \url{https://arxiv.org/abs/2103.10522} + +Säilynoja, T., Johnson, A., Martin, O., and Vehtari, A. (2026). +Recommendations for visual predictive checks in Bayesian workflow. +\emph{Journal of Visualization and Interaction}. 1(1). +doi:10.54337/jovi.v1i1.11478. + +Tesso, H., and Vehtari, A. (2026). LOO-PIT predictive model +checking. \emph{arXiv preprint arXiv:2603.02928}. +doi:10.48550/arXiv.2603.02928. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). \emph{Bayesian Data Analysis.} Chapman & Hall/CRC Press, London, third edition. (Ch. 6) - -Tesso, H., & Vehtari, A. (2026). LOO-PIT predictive model -checking. arXiv preprint https://arxiv.org/abs/2603.02928. } \seealso{ Other PPCs: diff --git a/man/PPC-loo.Rd b/man/PPC-loo.Rd index ecf5ee0a6..3a472eebb 100644 --- a/man/PPC-loo.Rd +++ b/man/PPC-loo.Rd @@ -405,13 +405,20 @@ Bayesian model evaluation using leave-one-out cross-validation and WAIC. doi:10.1007/s11222-016-9696-4. arXiv preprint: \url{https://arxiv.org/abs/1507.04544} +Säilynoja, T., Bürkner, P.-C., and Vehtari, A. (2022). +Graphical test for discrete uniformity and its applications in +goodness-of-fit evaluation and multiple sample comparison. +\emph{Statistics and Computing}. 32(2), 32. doi:10.1007/s11222-022-10090-6. +arXiv preprint: \url{https://arxiv.org/abs/2103.10522} + +Tesso, H., and Vehtari, A. (2026). LOO-PIT predictive model +checking. \emph{arXiv preprint arXiv:2603.02928}. +doi:10.48550/arXiv.2603.02928. + Boneva, L. I., Kendall, D., & Stefanov, I. (1971). Spline transformations: Three new diagnostic aids for the statistical data-analyst. \emph{J. R. Stat. Soc. B} (Methodological), 33(1), 1-71. https://www.jstor.org/stable/2986005. - -Tesso, H., & Vehtari, A. (2026). LOO-PIT predictive model -checking. arXiv preprint https://arxiv.org/abs/2603.02928. } \seealso{ Other PPCs: diff --git a/man/PPD-distributions.Rd b/man/PPD-distributions.Rd index 09604d635..a02bbe253 100644 --- a/man/PPD-distributions.Rd +++ b/man/PPD-distributions.Rd @@ -183,6 +183,12 @@ ppd_dens_overlay(ypred = preds[1:50, ]) ppd_dens_overlay(ypred = preds[1:50, ], show_marginal = TRUE) ppc_dens_overlay(y = example_y_data(), yrep = preds[1:50, ]) +} +\references{ +Säilynoja, T., Johnson, A., Martin, O., and Vehtari, A. (2026). +Recommendations for visual predictive checks in Bayesian workflow. +\emph{Journal of Visualization and Interaction}. 1(1). +doi:10.54337/jovi.v1i1.11478. } \seealso{ Other PPDs: diff --git a/vignettes/articles-online-only/loo-pit-correlated-tests.Rmd b/vignettes/articles-online-only/loo-pit-correlated-tests.Rmd index a57205092..1832a7e89 100644 --- a/vignettes/articles-online-only/loo-pit-correlated-tests.Rmd +++ b/vignettes/articles-online-only/loo-pit-correlated-tests.Rmd @@ -599,18 +599,18 @@ https://www.routledge.com/Bayesian-Workflow/Gelman-Vehtari-McElreath-Simpson-Mar Säilynoja, T., Bürkner, P.-C., and Vehtari, A. (2022). Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and -multiple sample comparison. *Statistics and Computing*, 32, 32. -https://link.springer.com/10.1007/s11222-022-10090-6 +multiple sample comparison. *Statistics and Computing*, 32(2), 32. +https://doi.org/10.1007/s11222-022-10090-6 Tesso, H., & Vehtari, A. (2026). LOO-PIT predictive model checking. -http://arxiv.org/abs/2603.02928 +*arXiv preprint arXiv:2603.02928*. https://doi.org/10.48550/arXiv.2603.02928 Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. *Statistics and Computing*, 27(5), 1413–1432. -https://link.springer.com/article/10.1007/S11222-016-9696-4 +https://doi.org/10.1007/s11222-016-9696-4 Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024). Pareto diff --git a/vignettes/articles-online-only/ppc-calibration.Rmd b/vignettes/articles-online-only/ppc-calibration.Rmd index ed90206c7..ae6d482c3 100644 --- a/vignettes/articles-online-only/ppc-calibration.Rmd +++ b/vignettes/articles-online-only/ppc-calibration.Rmd @@ -69,7 +69,7 @@ diagonal reference line. Deviations from the diagonal reveal miscalibration. To estimate the CEP from data, `bayesplot` uses the **Pool Adjacent Violators (PAV)** algorithm (Ayer et al., 1955). For more details see -Dimitriadis et al. (2021) and Säilynoja et al. (2025). The PAV algorithm solves +Dimitriadis et al. (2021) and Säilynoja et al. (2026). The PAV algorithm solves an isotonic regression problem: it finds the monotone non-decreasing step function that best fits the binary outcomes as a function of the ordered predicted probabilities. @@ -451,7 +451,7 @@ p1 + p2 + p3 + p4 + plot_layout(ncol = 2) Setting `show_qdots = TRUE` overlays a quantile dot plot along the x-axis, showing the marginal distribution of predicted probabilities. Each dot represents an empirical quantile. By default `qdots_quantiles = 100` dots are -displayed. See Säilynoja et al. (2025) for methodological details. +displayed. See Säilynoja et al. (2026) for methodological details. The following plot shows the calibration curves for the simulated calibrated and miscalibrated models, with quantile dot plots along the x-axis showing the distribution of predicted probabilities. The top row shows 100 quantiles, @@ -503,7 +503,7 @@ is available that produces a faceted plot when a grouping variable is supplied. ## Real-World Example: Modelling Roach Infestation -The following example is drawn from Säilynoja et al. (2025) and uses the +The following example is drawn from Säilynoja et al. (2026) and uses the `roaches` dataset from `rstanarm`. The data records the number of roaches caught in traps across 264 apartments assigned to a treatment or control condition. We compare two count-data models for the binary outcome "at least @@ -657,6 +657,7 @@ p1 + p2 *Proceedings of the National Academy of Sciences*, 118(8). https://doi.org/10.1073/pnas.2016191118 -- Säilynoja, T., Johnson, A. R., Martin, O. A., & Vehtari, A. (2025). +- Säilynoja, T., Johnson, A., Martin, O., & Vehtari, A. (2026). Recommendations for visual predictive checks in Bayesian workflow. - (Preprint). *arXiv*. https://doi.org/10.48550/arXiv.2503.01509 + *Journal of Visualization and Interaction*, 1(1). + https://doi.org/10.54337/jovi.v1i1.11478 From eb382da977715f8c20997dca119f58f679980ae7 Mon Sep 17 00:00:00 2001 From: Aki Vehtari Date: Tue, 6 Oct 2026 23:16:36 +0300 Subject: [PATCH 2/3] add two missing files --- man-roxygen/reference-loo-pit-tests.R | 3 +++ man-roxygen/reference-vpc-recommendations.R | 4 ++++ 2 files changed, 7 insertions(+) create mode 100644 man-roxygen/reference-loo-pit-tests.R create mode 100644 man-roxygen/reference-vpc-recommendations.R diff --git a/man-roxygen/reference-loo-pit-tests.R b/man-roxygen/reference-loo-pit-tests.R new file mode 100644 index 000000000..86bab36de --- /dev/null +++ b/man-roxygen/reference-loo-pit-tests.R @@ -0,0 +1,3 @@ +#' @references Tesso, H., and Vehtari, A. (2026). LOO-PIT predictive model +#' checking. *arXiv preprint arXiv:2603.02928*. +#' doi:10.48550/arXiv.2603.02928. diff --git a/man-roxygen/reference-vpc-recommendations.R b/man-roxygen/reference-vpc-recommendations.R new file mode 100644 index 000000000..367052190 --- /dev/null +++ b/man-roxygen/reference-vpc-recommendations.R @@ -0,0 +1,4 @@ +#' @references Säilynoja, T., Johnson, A., Martin, O., and Vehtari, A. (2026). +#' Recommendations for visual predictive checks in Bayesian workflow. +#' *Journal of Visualization and Interaction*. 1(1). +#' doi:10.54337/jovi.v1i1.11478. From acb929e72a83a458d98a5a0f0f86a4516897ed59 Mon Sep 17 00:00:00 2001 From: Aki Vehtari Date: Tue, 6 Oct 2026 23:17:39 +0300 Subject: [PATCH 3/3] use YEAR for the package citation --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c042247c2..151df85fc 100644 --- a/README.md +++ b/README.md @@ -56,7 +56,7 @@ appropriately so that developers get credit for their work. When using **bayesplot**, please cite it as follows: -* Gabry J, Mahr T (2026). bayesplot: Plotting for Bayesian Models. R package +* Gabry J, Mahr T (YEAR). bayesplot: Plotting for Bayesian Models. R package version XXX, . * Gabry J, Simpson D, Vehtari A, Betancourt M, Gelman A (2019). Visualization