update psis paper reference

R/diagnostics.R

@@ -6,7 +6,7 @@
 #' `threshold` value, or plot observation indexes vs. diagnostic estimates.
 #' The **Details** section below provides a brief overview of the
 #' diagnostics, but we recommend consulting Vehtari, Gelman, and Gabry (2017)
-#' and Vehtari, Simpson, Gelman, Yao, and Gabry (2022) for full details.
+#' and Vehtari, Simpson, Gelman, Yao, and Gabry (2024) for full details.
 #'
 #' @name pareto-k-diagnostic
 #' @param x An object created by [loo()] or [psis()].
@@ -23,7 +23,7 @@
 #' parameter \eqn{k} of the generalized Pareto distribution. The
 #' diagnostic threshold for Pareto \eqn{k} depends on sample size
 #' \eqn{S} (sample size dependent threshold was introduced by Vehtari
-#' et al. (2022), and before that fixed thresholds of 0.5 and 0.7 were
+#' et al. (2024), and before that fixed thresholds of 0.5 and 0.7 were
 #' recommended). For simplicity, `loo` package uses the nominal sample
 #' size \eqn{S} when computing the sample size specific
 #' threshold. This provides an optimistic threshold if the effective
@@ -99,7 +99,7 @@
 #' obtain the samples from the proposal distribution via MCMC the **loo**
 #' package also computes estimates for the Monte Carlo error and the effective
 #' sample size for importance sampling, which are more accurate for PSIS than
-#' for IS and TIS (see Vehtari et al (2022) for details). However, the PSIS
+#' for IS and TIS (see Vehtari et al (2024) for details). However, the PSIS
 #' effective sample size estimate will be
 #' **over-optimistic when the estimate of \eqn{k} is greater than**
 #' \eqn{min(1-1/log10(S), 0.7)}, where \eqn{S} is the sample size.

R/loo-glossary.R

@@ -76,7 +76,7 @@
 #'
 #' The diagnostic threshold for Pareto \eqn{k} depends on sample size
 #' \eqn{S} (sample size dependent threshold was introduced by Vehtari
-#' et al., 2022, and before that fixed thresholds of 0.5 and 0.7 were
+#' et al., 2024, and before that fixed thresholds of 0.5 and 0.7 were
 #' recommended). For simplicity, `loo` package uses the nominal sample
 #' size \eqn{S}  when computing the sample size specific
 #' threshold. This provides an optimistic threshold if the effective

R/loo-package.R

@@ -13,7 +13,7 @@
 #' *Stan Development Team*
 #'
 #' This package implements the methods described in Vehtari, Gelman, and
-#' Gabry (2017), Vehtari, Simpson, Gelman, Yao, and Gabry (2022), and
+#' Gabry (2017), Vehtari, Simpson, Gelman, Yao, and Gabry (2024), and
 #' Yao et al. (2018). To get started see the **loo** package
 #' [vignettes](https://mc-stan.org/loo/articles/index.html), the
 #' [loo()] function for efficient approximate leave-one-out
@@ -33,7 +33,7 @@
 #'   fast and stable computations for approximate LOO-CV laid out in Vehtari,
 #'   Gelman, and Gabry (2017). From existing posterior simulation draws, we
 #'   compute LOO-CV using Pareto smoothed importance sampling (PSIS; Vehtari,
-#'   Simpson, Gelman, Yao, and Gabry, 2022), a new procedure for stabilizing
+#'   Simpson, Gelman, Yao, and Gabry, 2024), a new procedure for stabilizing
 #'   and diagnosing importance weights. As a byproduct of our calculations,
 #'   we also obtain approximate standard errors for estimated predictive
 #'   errors and for comparing of predictive errors between two models.

R/loo.R

@@ -4,7 +4,7 @@
 #' CV, efficient approximate leave-one-out (LOO) cross-validation for Bayesian
 #' models using Pareto smoothed importance sampling ([PSIS][psis()]). This is
 #' an implementation of the methods described in Vehtari, Gelman, and Gabry
-#' (2017) and Vehtari, Simpson, Gelman, Yao, and Gabry (2022).
+#' (2017) and Vehtari, Simpson, Gelman, Yao, and Gabry (2024).
 #'
 #' @export loo loo.array loo.matrix loo.function
 #' @param x A log-likelihood array, matrix, or function. The **Methods (by class)**
@@ -500,7 +500,7 @@ mcse_elpd <- function(ll, lw, E_elpd, r_eff, n_samples = NULL) {
       FUN.VALUE = numeric(1),
       FUN = function(i) {
         # Variance in linear scale
-        # Equation (6) in Vehtari et al. (2022)
+        # Equation (6) in Vehtari et al. (2024)
         var_epd_i <- sum(w2[, i] * (lik[, i] - E_epd[i]) ^ 2) / r_eff[i]
         # Compute variance in log scale by match the variance of a
         # log-normal approximation

R/loo_model_weights.R

@@ -3,7 +3,7 @@
 #' Model averaging via stacking of predictive distributions, pseudo-BMA
 #' weighting or pseudo-BMA+ weighting with the Bayesian bootstrap. See Yao et
 #' al. (2018), Vehtari, Gelman, and Gabry (2017), and Vehtari, Simpson,
-#' Gelman, Yao, and Gabry (2022) for background.
+#' Gelman, Yao, and Gabry (2024) for background.
 #'
 #' @export
 #' @param x A list of `"psis_loo"` objects (objects returned by [loo()]) or

R/psis.R

@@ -3,7 +3,7 @@
 #' Implementation of Pareto smoothed importance sampling (PSIS), a method for
 #' stabilizing importance ratios. The version of PSIS implemented here
 #' corresponds to the algorithm presented in Vehtari, Simpson, Gelman, Yao,
-#' and Gabry (2022).
+#' and Gabry (2024).
 #' For PSIS diagnostics see the [pareto-k-diagnostic] page.
 #'
 #' @export

R/waic.R

@@ -29,7 +29,7 @@
 #' @seealso
 #' * The __loo__ package [vignettes](https://mc-stan.org/loo/articles/) and
 #'   Vehtari, Gelman, and Gabry (2017) and Vehtari, Simpson, Gelman, Yao,
-#'   and Gabry (2022) for more details on why we prefer `loo()` to `waic()`.
+#'   and Gabry (2024) for more details on why we prefer `loo()` to `waic()`.
 #' * [loo_compare()] for comparing models on approximate LOO-CV or WAIC.
 #'
 #' @references

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

@@ -5,8 +5,9 @@
 #' ([journal version](https://link.springer.com/article/10.1007/s11222-016-9696-4),
 #'  [preprint arXiv:1507.04544](https://arxiv.org/abs/1507.04544)).
 #'
-#' Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022).
-#' Pareto smoothed importance sampling.
+#' Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+#' Pareto smoothed importance sampling. *Journal of Machine Learning Research*,
+#' accepted for publication.
 #' [preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
 #'
 #' Sivula, T, Magnusson, M., Matamoros A. A., and Vehtari, A. (2022).

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

@@ -6,6 +6,7 @@
 #'  [preprint arXiv:1507.04544](https://arxiv.org/abs/1507.04544)).
 #'
 #' Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022).
-#' Pareto smoothed importance sampling.
+#' Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+#' accepted for publication.
 #' [preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
 #'