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..151df85fc 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 (YEAR). bayesplot: Plotting for Bayesian Models. R package
+  version XXX, <https://mc-stan.org/bayesplot/>.
+
+* 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-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-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: <https://arxiv.org/abs/2103.10522>
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.
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
 <a id="Säilynoja2022"></a>
 
 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
 <a id="Tesso2026"></a>
 
 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
 <a id="Vehtari2017"></a>
 
 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