update psis ref + some minor typo fixes

Author: avehtari

R/loo_moment_matching.R

@@ -195,7 +195,7 @@ loo_moment_match.default <- function(x, loo, post_draws, log_lik_i,
 #' @param i observation number.
 #' @param x A fitted model object.
 #' @param log_lik_i A function that takes `x` and `i` and returns a matrix (one
-#'   column per chain) or a vector (all chains stacked) of log-likeliood draws
+#'   column per chain) or a vector (all chains stacked) of log-likelihood draws
 #'   of the `i`th observation based on the model `x`. If the draws are obtained
 #'   using MCMC, the matrix with MCMC chains separated is preferred.
 #' @param unconstrain_pars A function that takes arguments `x`, and `pars` and
@@ -440,7 +440,7 @@ loo_moment_match_i <- function(i,
 #'   `upars` and returns a matrix of log-posterior density values of the
 #'   unconstrained posterior draws passed via `upars`.
 #' @param log_lik_i_upars A function that takes arguments `x`, `upars`,
-#'   and `i` and returns a vector of log-likeliood draws of the `i`th
+#'   and `i` and returns a vector of log-likelihood draws of the `i`th
 #'   observation based on the unconstrained posterior draws passed via
 #'   `upars`.
 #' @param r_eff_i MCMC effective sample size divided by the total sample size

R/loo_subsample.R

@@ -316,7 +316,7 @@ update.psis_loo_ss <- function(object, ...,
       stopifnot(is.data.frame(data) || is.matrix(data) & !is.null(draws))
     }
 
-    # Compute subsample indecies
+    # Compute subsample indices
     if (length(observations) > 1) {
       idxs <- compute_idxs(observations)
     } else {
@@ -448,7 +448,7 @@ nobs.psis_loo_ss <- function(object, ...) {
 #' @details
 #' The choice `psis` is returned if a `psis_loo` object
 #' is converted to a `psis_loo_ss` object with `as.psis_loo_ss()`.
-#' But `psis` cannot be chosen in the api of `loo_subsample()`.
+#' But `psis` cannot be chosen in the API of `loo_subsample()`.
 #'
 #' @noRd
 #' @param api The choices available in the loo API or all possible choices.
@@ -756,7 +756,7 @@ compute_idxs <- function(observations) {
 }
 
 
-#' Compare the indecies to prepare handling
+#' Compare the indices to prepare handling
 #'
 #' @details
 #' The function compares the object and sampled indices into `new`
@@ -1193,14 +1193,15 @@ srs_diff_est <- function(y_approx, y, y_idx) {
   # eq (9) first row second `+` should be `-`
   # Supplementary material eq (6) has this correct
   # Here the variance is for sum, while in the paper the variance is for mean
-  # which explains the proporional difference of 1/n
+  # which explains the proportional difference of 1/N
   est_list$hat_v_y <- (t_pi2_tilde + t_hat_epsilon) - # a (has been checked)
     (1/N) * (t_e^2 - est_list$v_y_hat + 2 * t_pi_tilde * est_list$y_hat - t_pi_tilde^2) # b
   est_list
 }
 
 
-#' Estimate elpd using the standard SRS estimator and SRS WOR
+#' Estimate elpd using the standard simple-re-sample without
+#' resampling (SRS-WOR) estimator
 #' @noRd
 #' @param x A `psis_loo_ss` object.
 #' @return A `psis_loo_ss` object.
@@ -1220,7 +1221,7 @@ loo_subsample_estimation_srs <- function(x) {
   update_psis_loo_ss_estimates(x)
 }
 
-#' Simple SRS-WOR estimation
+#' Simple-re-sample without resampling (SRS-WOR) estimation
 #' @noRd
 #' @param y The values observed.
 #' @param y_approx A vector of length N.
@@ -1324,6 +1325,3 @@ assert_subsampling_pointwise <- function(x) {
   checkmate::assert_names(colnames(x), identical.to = c("elpd_loo", "mcse_elpd_loo", "p_loo", "looic", "influence_pareto_k", "idx", "m_i", "elpd_loo_approx"))
   x
 }
-
-
-

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

@@ -7,8 +7,8 @@
 #'
 #' 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)
+#' 25(72):1-58.
+#' [PDF](https://jmlr.org/papers/v25/19-556.html)
 #'
 #' Sivula, T, Magnusson, M., Matamoros A. A., and Vehtari, A. (2022).
 #' Uncertainty in Bayesian leave-one-out cross-validation based model

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

@@ -6,7 +6,7 @@
 #'  [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*,
-#' accepted for publication.
-#' [preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
+#' Pareto smoothed importance sampling. *Journal of Machine Learning Research*,
+#' 25(72):1-58.
+#' [PDF](https://jmlr.org/papers/v25/19-556.html)
 #'

vignettes/loo2-example.Rmd

@@ -32,8 +32,7 @@ encourage readers to refer to the following papers for more details:
 
 * Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
 Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
-accepted for publication.
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
+25(72):1-58. [PDF](https://jmlr.org/papers/v25/19-556.html)
 
 
 # Setup
@@ -302,5 +301,4 @@ Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4.
 
 Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
 Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
-accepted for publication.
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
+25(72):1-58. [PDF](https://jmlr.org/papers/v25/19-556.html)

vignettes/loo2-large-data.Rmd

@@ -37,8 +37,7 @@ Proceedings of the 23rd International Conference on Artificial Intelligence and
 
 * Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
 Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
-accepted for publication.
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
+25(72):1-58. [PDF](https://jmlr.org/papers/v25/19-556.html)
 
 which provide important background for understanding the methods implemented in
 the package.
@@ -613,5 +612,4 @@ Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4.
 
 Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
 Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
-accepted for publication.
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
+25(72):1-58. [PDF](https://jmlr.org/papers/v25/19-556.html)

vignettes/loo2-lfo.Rmd

@@ -642,8 +642,7 @@ Vehtari A., Gelman A., & Gabry J. (2017). Practical Bayesian model evaluation us
 
 Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
 Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
-accepted for publication.
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
+25(72):1-58. [PDF](https://jmlr.org/papers/v25/19-556.html)
 
 <br />
 

vignettes/loo2-mixis.Rmd

@@ -197,8 +197,7 @@ Vehtari A., Gelman A., and Gabry J. (2017). Practical Bayesian model evaluation
 
 Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
 Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
-accepted for publication.
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
+25(72):1-58. [PDF](https://jmlr.org/papers/v25/19-556.html)
 
 
 

vignettes/loo2-moment-matching.Rmd

@@ -45,8 +45,7 @@ papers
 
 * Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
 Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
-accepted for publication.
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
+25(72):1-58. [PDF](https://jmlr.org/papers/v25/19-556.html)
 
 # Example: Eradication of Roaches
 
@@ -322,5 +321,4 @@ Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluati
 
 Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
 Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
-accepted for publication.
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
+25(72):1-58. [PDF](https://jmlr.org/papers/v25/19-556.html)

vignettes/loo2-non-factorized.Rmd

@@ -718,5 +718,4 @@ Vehtari A., Gelman A., & Gabry J. (2017). Practical Bayesian model evaluation us
 
 Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
 Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
-accepted for publication.
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
+25(72):1-58. [PDF](https://jmlr.org/papers/v25/19-556.html)