add more citations

README.Rmd

@@ -263,14 +263,40 @@ When using **posterior**, please cite it as follows:
 Working with Posterior Distributions.” R package version XXX, <URL:
 https://mc-stan.org/posterior/>.
 
-When using the MCMC convergence diagnostics `rhat`, `ess_bulk`, or `ess_tail`,
+When using the MCMC convergence diagnostics `rhat`, `ess_bulk`, `ess_tail`,
+`ess_median`, `ess_quantile`, `mcse_median`, or `mcse_quantile`
 please also cite
 
 * Vehtari A., Gelman A., Simpson D., Carpenter B., & Bürkner P. C. (2021).
 Rank-normalization, folding, and localization: An improved Rhat for assessing
 convergence of MCMC (with discussion). *Bayesian Analysis*. 16(2), 667–718.
 doi.org/10.1214/20-BA1221
 
+When using the MCMC convergence diagnostic `rhat_nested`
+please also cite
+
+* Margossian, C. C., Hoffman, M. D., Sountsov, P., Riou-Durand, L.,
+  Vehtari, A., and Gelman, A. (2024).
+  Nested $\widehat{R}$: Assessing the convergence of Markov chain
+  Monte Carlo when running many short chains. *Bayesian Analysis*,
+  doi:10.1214/24-BA1453. 
+
+When using the MCMC convergence diagnostic `rstar`
+please also cite
+
+* Lambert, B. and Vehtari, A. (2022). $R^*$: A robust MCMC convergence
+  diagnostic with uncertainty using decision tree classifiers.
+  *Bayesian Analysis*, 17(2):353-379.
+  doi:10.1214/20-BA1252
+
+When using the Pareto-k diagnostics `pareto_khat`, `pareto_min_ss`,
+`pareto_convergence_rate`, `khat_threshold` or `pareto_diags`, or
+Pareto smoothing `pareto_smooth` please also cite
+
+* 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.
+
 The same information can be obtained by running `citation("posterior")`.
 
 ### References
@@ -279,11 +305,26 @@ Gelman A., Carlin J. B., Stern H. S., David B. Dunson D. B., Aki Vehtari A.,
 & Rubin D. B. (2013). *Bayesian Data Analysis, Third Edition*. Chapman and
 Hall/CRC.
 
+Lambert, B. and Vehtari, A. (2022). $R^*$: A robust MCMC convergence
+diagnostic with uncertainty using decision tree classifiers.
+*Bayesian Analysis*, 17(2):353-379.
+doi:10.1214/20-BA1252
+
+Margossian, C. C., Hoffman, M. D., Sountsov, P., Riou-Durand, L.,
+Vehtari, A., and Gelman, A. (2024).
+Nested $\widehat{R}$: Assessing the convergence of Markov chain
+Monte Carlo when running many short chains. *Bayesian Analysis*,
+doi:10.1214/24-BA1453.
+
 Vehtari A., Gelman A., Simpson D., Carpenter B., & Bürkner P. C. (2021).
 Rank-normalization, folding, and localization: An improved Rhat for assessing
 convergence of MCMC (with discussion). *Bayesian Analysis*. 16(2), 667–718.
 doi.org/10.1214/20-BA1221
 
+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.
+
 ### Licensing
 
 The **posterior** package is licensed under the following licenses:

inst/CITATION

@@ -21,7 +21,64 @@ for assessing convergence of MCMC (with discussion)",
                     person("Daniel", "Simpson"),
                     person("Bob", "Carpenter"),
                     person("Paul-Christian", "Bürkner")),
-         year = "2021",
          journal = "Bayesian Analysis",
-         header = "To cite the MCMC convergence diagnostics:"
+         year = "2021",
+         volume = "16",
+         number = "2",
+         pages = "667-718",
+         header = "To cite the MCMC convergence diagnostics (`rhat`, `ess_bulk`, `ess_tail`,
+ `ess_median`, `ess_quantile`, `mcse_median`, and `mcse_quantile`):"
+)
+
+bibentry(
+  title = "Nested Rhat: Assessing the convergence of Markov chain Monte Carlo when running many short chains",
+  bibtype = "Article",
+  author = c(
+    person("Charles C.", "Margossian"),
+    person("Matthew D.", "Hoffman"),
+    person("Pavel", "Sountsov"),
+    person("Lionel", "Riou-Durand"),
+    person("Aki", "Vehtari"),
+    person("Andrew", "Gelman")
+  ),
+  journal = "Bayesian Analysis",
+  year = 2024,
+  doi = "10.1214/24-BA1453",
+  header = "To cite MCMC convergence diagnostic `nested_rhat`:"
+)
+
+bibentry(
+  title = "Rstar: A robust MCMC convergence diagnostic with uncertainty using decision tree classifiers",
+  bibtype = "Article",
+  author = c(
+    person("Ben", "Lambert"),
+    person("Aki", "Vehtari")
+  ),
+  journal = "Bayesian Analysis",
+  year = 2022,
+  volume = 17,
+  number = 2,
+  pages = "353-379",
+  doi = "10.1214/20-BA1252",
+  header = "To cite MCMC convergence diagnostic `rstar`:"
+)
+
+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 and Pareto smoothing (`pareto_khat`, `pareto_min_ss`,
+`pareto_convergence_rate`, `khat_threshold`, `pareto_diags`, and
+`pareto_smooth`):"
 )