update references

Author: avehtari

README.md

@@ -13,20 +13,25 @@ __loo__ is an R package that allows users to compute efficient approximate
 leave-one-out cross-validation for fitted Bayesian models, as well as model
 weights that can be used to average predictive distributions. 
 The __loo__ package package implements the fast and stable computations for 
-approximate LOO-CV and WAIC from
+approximate LOO-CV
 
 * 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. 
-doi:10.1007/s11222-016-9696-4. [Online](https://link.springer.com/article/10.1007/s11222-016-9696-4), 
+doi:10.1007/s11222-016-9696-4. [Online](https://dx.doi.org/10.1007/s11222-016-9696-4), 
 [arXiv preprint arXiv:1507.04544](https://arxiv.org/abs/1507.04544).
 
+* Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling. *Journal of Machine Learning Research*,
+25(72):1-58.
+[Online](https://jmlr.org/papers/v25/19-556.html)
+
 and computes model weights as described in
 
 * Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018). Using
 stacking to average Bayesian predictive distributions. In Bayesian
 Analysis, doi:10.1214/17-BA1091. 
-[Online](https://projecteuclid.org/euclid.ba/1516093227),
+[Online](https://dx.doi.org/10.1214/17-BA1091),
 [arXiv preprint arXiv:1704.02030](https://arxiv.org/abs/1704.02030).
 
 From existing posterior simulation draws, we compute approximate LOO-CV using

inst/CITATION

@@ -26,6 +26,18 @@ bibentry(bibtype = "Article",
          header = "To cite the loo paper:"
 )
 
+bibentry(bibtype = "Article",
+         title = "Uncertainty in Bayesian leave-one-out cross-validation based model comparison",
+         author = c(person("Tuomas", "Sivula"),
+                    person("Måns", "Magnusson"),
+                    person("Asael Alonzo", "Matamoros"),
+    		    person("Aki", "Vehtari")),
+         journal = "Bayesian Analysis",
+         year = "2025",
+         note = "accepted for publication",
+         header = "To cite when using loo_compare():"
+)
+
 bibentry(bibtype = "Article",
          title = "Using stacking to average Bayesian predictive distributions",
          author = c(person("Yuling", "Yao"),
@@ -40,3 +52,71 @@ bibentry(bibtype = "Article",
          doi = "10.1214/17-BA1091",
          header = "To cite the stacking paper:"
 )
+
+bibentry(
+  title = "Pareto smoothed importance sampling",
+  bibtype = "Article",
+  author = c(
+    person("Aki", "Vehtari"),
+    person("Daniel", "Simpson"),
+    person("Andrew", "Gelman"),
+    person("Yuling", "Yao"),
+    person("Jonah", "Gabry")
+  ),
+  journal = "Journal of Machine Learning Research",
+  year = 2024,
+  volume = 25,
+  number = 72,
+  pages = "1-58",
+  header = "To cite Pareto-k diagnostics:"
+)
+
+bibentry(
+  bibtype = "Article",
+  author = c(
+    person(given = "Topi", family = "Paananen"),
+    person(given = "Juho", family = "Piironen"),
+    person(given = "Paul-Christian", family = "Buerkner"),
+    person(given = "Aki", family = "Vehtari")
+  ),
+  title = "Implicitly adaptive importance sampling",
+  journal = "Statistics and Computing",
+  volume = 31,
+  pages = "16",
+  year = 2021,
+  header = "To cite moment matching:"
+)
+
+bibentry(
+  bibtype = "InProceedings",
+  author = c(
+    person(given = "Måns", family = "Magnusson"),
+    person(given = "Michael Riis", family = "Andersen"),
+    person(given = "Johan", family = "Jonasson"),
+    person(given = "Aki", family = "Vehtari")
+  ),
+  title = "Leave-One-Out Cross-Validation for Large Data",
+  booktitle = "Thirty-sixth International Conference on Machine Learning",
+  publisher = "PMLR",
+  volume = "97",
+  pages = "4244-4253",
+  year = 2019,
+  header = "To cite subsampling loo:"
+)
+
+bibentry(
+  bibtype = "InProceedings",
+  author = c(
+    person(given = "Måns", family = "Magnusson"),
+    person(given = "Michael Riis", family = "Andersen"),
+    person(given = "Johan", family = "Jonasson"),
+    person(given = "Aki", family = "Vehtari")
+  ),
+  title = "Leave-One-Out Cross-Validation for Model Comparison in Large Data",
+  booktitle = "Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS)",
+  publisher = "PMLR",
+  volume = "108",
+  pages = "341-351",
+  year = 2019,
+  header = "To cite subsampling loo:"
+)

man-roxygen/loo-and-compare-references.R

@@ -10,10 +10,12 @@
 #' 25(72):1-58.
 #' [PDF](https://jmlr.org/papers/v25/19-556.html)
 #'
-#' Sivula, T, Magnusson, M., Matamoros A. A., and Vehtari, A. (2022).
+#' Sivula, T, Magnusson, M., Matamoros A. A., and Vehtari, A. (2025).
 #' Uncertainty in Bayesian leave-one-out cross-validation based model
-#' comparison. [preprint arXiv:2008.10296v3.](https://arxiv.org/abs/2008.10296v3).
+#' comparison. *Bayesian Analysis*, accepted for publication.
+#' [preprint arXiv:2008.10296v5.](https://arxiv.org/abs/2008.10296v5).
 #' 
-#' McLatchie, Y., and Vehtari, A. (2023).  Efficient estimation and
+#' McLatchie, Y., and Vehtari, A. (2024).  Efficient estimation and
 #' correction of selection-induced bias with order statistics.
-#' [preprint arXiv:2309.03742](https://arxiv.org/abs/2309.03742)
+#' *Statistics and Computing*. 34(132). 
+#' [doi:10.1007/s11222-024-10442-4](https://doi.org/10.1007/s11222-024-10442-4)

man-roxygen/loo-uncertainty-reference.R

@@ -1,4 +1,5 @@
-#' @references Sivula, T, Magnusson, M., Matamoros A. A., and Vehtari,
-#'   A. (2022).  Uncertainty in Bayesian leave-one-out
-#'   cross-validation based model comparison. [preprint
-#'   arXiv:2008.10296v3.](https://arxiv.org/abs/2008.10296v3).
+#' @references
+#' Sivula, T, Magnusson, M., Matamoros A. A., and Vehtari, A. (2025).
+#' Uncertainty in Bayesian leave-one-out cross-validation based model
+#' comparison. *Bayesian Analysis*, accepted for publication.
+#' [preprint arXiv:2008.10296v5.](https://arxiv.org/abs/2008.10296v5).