Merge branch 'master' into gh-pages

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 2.7.0
-Date: 2024-02-24 21:45:11 UTC
-SHA: e1f9a29e68f937d992861d40df7e48421204e05b
+Version: 2.8.0
+Date: 2024-07-03 16:27:12 UTC
+SHA: 23e68bf5fb8aa9d000358f3bbbf52cf7ee92a99c

DESCRIPTION

@@ -1,11 +1,11 @@
 Package: loo
 Type: Package
 Title: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models
-Version: 2.7.0
-Date: 2024-02-24
+Version: 2.8.0
+Date: 2024-07-03
 Authors@R: c(person("Aki", "Vehtari", email = "[email protected]", role = c("aut")),
              person("Jonah", "Gabry", email = "[email protected]", role = c("cre", "aut")),
-             person("Mans", "Magnusson", role = c("aut")),
+             person("Måns", "Magnusson", role = c("aut")),
              person("Yuling", "Yao", role = c("aut")),
              person("Paul-Christian", "Bürkner", role = c("aut")),
              person("Topi", "Paananen", role = c("aut")),
@@ -53,5 +53,5 @@ Suggests:
 VignetteBuilder: knitr
 Encoding: UTF-8
 SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc
-RoxygenNote: 7.3.0
+RoxygenNote: 7.3.1
 Roxygen: list(markdown = TRUE)

NEWS.md

@@ -1,29 +1,43 @@
+# loo 2.8.0
+
+* make E_loo Pareto-k diagnostic more robust by @avehtari in #251
+* update psis paper reference by @avehtari in #252
+* update PSIS references in vignettes by @jgabry in #254
+* fix loo_moment_match p_loo computation by @avehtari in #257
+* fix loo_moment_matching NaN issue by @avehtari in #259
+* catch Stan log_prob exceptions inside moment matching by @avehtari in #262
+* Fix E_loo_khat error when posterior::pareto_khat returns NA by @jgabry in #264
+* update psis ref + some minor typo fixes by @avehtari in #266
+* update PSIS ref + link to Nabiximols study for Jacobian correction by @avehtari in #267
+* Fix issue with pareto_khat output no longer being a list by @n-kall in #269
+* fix equations in loo-glossary by @avehtari in #268
+
 # loo 2.7.0
 
 ### Major changes
 
 * __New sample size specific diagnostic threshold for Pareto `k`__. The pre-2022 version
 of the [PSIS paper](https://arxiv.org/abs/1507.02646) recommended diagnostic
-thresholds of 
-`k < 0.5 "good"`, `0.5 <= k < 0.7 "ok"`, 
-`0.7 <= k < 1 "bad"`, `k>=1 "very bad"`. 
-The 2022 revision of the PSIS paper now recommends 
-`k < min(1 - 1/log10(S), 0.7) "good"`, `min(1 - 1/log10(S), 0.7) <= k < 1 "bad"`, 
-`k > 1 "very bad"`, where `S` is the sample size. 
+thresholds of
+`k < 0.5 "good"`, `0.5 <= k < 0.7 "ok"`,
+`0.7 <= k < 1 "bad"`, `k>=1 "very bad"`.
+The 2022 revision of the PSIS paper now recommends
+`k < min(1 - 1/log10(S), 0.7) "good"`, `min(1 - 1/log10(S), 0.7) <= k < 1 "bad"`,
+`k > 1 "very bad"`, where `S` is the sample size.
 There is now one fewer diagnostic threshold (`"ok"` has been removed), and the
 most important threshold now depends on the sample size `S`. With sample sizes
-`100`, `320`, `1000`, `2200`, `10000` the sample size specific part 
+`100`, `320`, `1000`, `2200`, `10000` the sample size specific part
 `1 - 1/log10(S)` corresponds to thresholds of `0.5`, `0.6`, `0.67`, `0.7`, `0.75`.
-Even if the sample size grows, the bias in the PSIS estimate dominates if 
-`0.7 <= k < 1`, and thus the diagnostic threshold for good is capped at 
-`0.7` (if `k > 1`, the mean does not exist and bias is not a valid measure). 
+Even if the sample size grows, the bias in the PSIS estimate dominates if
+`0.7 <= k < 1`, and thus the diagnostic threshold for good is capped at
+`0.7` (if `k > 1`, the mean does not exist and bias is not a valid measure).
 The new recommended thresholds are based on more careful bias-variance analysis
 of PSIS based on truncated Pareto sums theory. For those who use the Stan
 default 4000 posterior draws, the `0.7` threshold will be roughly the same, but
 there will be fewer warnings as there will be no diagnostic message for `0.5 <=
 k < 0.7`. Those who use smaller sample sizes may see diagnostic messages with a
 threshold less than `0.7`, and they can simply increase the sample size to about
-`2200` to get the threshold to `0.7`. 
+`2200` to get the threshold to `0.7`.
 
 * __No more warnings if the `r_eff` argument is not provided__, and the
 default is now `r_eff = 1`. The summary print output showing MCSE and ESS now
@@ -55,10 +69,10 @@ only when MCSE and ESS values are shown.
 * fix R cmd check notes by @jgabry in #242
 
 
- 
+
 # loo 2.6.0
 
-### New features 
+### New features
 
 * New `loo_predictive_metric()` function for computing estimates of leave-one-out
 predictive metrics: mean absolute error, mean squared error and root mean

R/E_loo.R

@@ -288,11 +288,23 @@ E_loo_khat.matrix <- function(x, psis_object, log_ratios, ...) {
 .E_loo_khat_i <- function(x_i, log_ratios_i, tail_len_i) {
   h_theta <- x_i
   r_theta <- exp(log_ratios_i - max(log_ratios_i))
-  khat_r <- posterior::pareto_khat(r_theta, tail = "right", ndraws_tail = tail_len_i)$khat
-  if (is.null(x_i)) {
+  khat_r <- posterior::pareto_khat(r_theta, tail = "right", ndraws_tail = tail_len_i)
+  if (is.list(khat_r)) { # retain compatiblity with older posterior that returned a list
+    khat_r <- khat_r$khat
+  }
+  if (is.null(x_i) || is_constant(x_i) || length(unique(x_i))==2 ||
+        anyNA(x_i) || any(is.infinite(x_i))) {
     khat_r
   } else {
-    khat_hr <- posterior::pareto_khat(h_theta * r_theta, tail = "both", ndraws_tail = tail_len_i)$khat
-    max(khat_hr, khat_r)
+    khat_hr <- posterior::pareto_khat(h_theta * r_theta, tail = "both", ndraws_tail = tail_len_i)
+    if (is.list(khat_hr)) { # retain compatiblity with older posterior that returned a list
+      khat_hr <- khat_hr$khat
+    }
+    if (is.na(khat_hr) && is.na(khat_r)) {
+      k <- NA
+    } else {
+      k <- max(khat_hr, khat_r, na.rm=TRUE)
+    }
+    k
   }
 }

R/loo-glossary.R

@@ -84,15 +84,15 @@
 #' is usually negligible. Thinning of MCMC draws can be used to improve
 #' the ratio ESS/S.
 #'
-#' * If \eqn{k < min(1 - 1 / log10(S), 0.7)}, where \eqn{S} is the
+#' * If \eqn{k < \min(1 - 1 / \log_{10}(S), 0.7)}, where \eqn{S} is the
 #'   sample size, the PSIS estimate and the corresponding Monte
 #'   Carlo standard error estimate are reliable.
 #'
-#' * If \eqn{1 - 1 / log10(S) <= k < 0.7}, the PSIS estimate and the
+#' * If \eqn{1 - 1 / \log_{10}(S) <= k < 0.7}, the PSIS estimate and the
 #'   corresponding Monte Carlo standard error estimate are not
 #'   reliable, but increasing the (effective) sample size \eqn{S} above
 #'   2200 may help (this will increase the sample size specific
-#'   threshold \eqn{(1 - 1 / log10(2200) > 0.7} and then the bias specific
+#'   threshold \eqn{(1 - 1 / \log_{10}(2200) > 0.7} and then the bias specific
 #'   threshold 0.7 dominates).
 #'
 #' * If \eqn{0.7 <= k < 1}, the PSIS estimate and the corresponding Monte