Fix bug in IS method error message + documentation typos

.gitattributes

@@ -1,4 +1,6 @@
 * text=auto
 data/* binary
-src/* text=lf
-R/* text=lf
+src/* text eol=lf
+R/* text eol=lf
+*.rda binary
+

R/diagnostics.R

@@ -11,7 +11,7 @@
 #' @name pareto-k-diagnostic
 #' @param x An object created by [loo()] or [psis()].
 #' @param threshold For `pareto_k_ids()`, `threshold` is the minimum \eqn{k}
-#'   value to flag (default is a sample size `S` dependend threshold
+#'   value to flag (default is a sample size `S` dependent threshold
 #'   `1 - 1 / log10(S)`). For `mcse_loo()`, if any \eqn{k} estimates are
 #'   greater than `threshold` the MCSE estimate is returned as `NA`
 #'   See **Details** for the motivation behind these defaults.
@@ -430,7 +430,7 @@ min_n_eff_by_k <- function(n_eff, kcut) {
 #' with bigger sample size S we can achieve estimates with small
 #' probability of large error, it is difficult to get accurate MCSE
 #' estimates as the bias starts to dominate when k > 0.7 (see Section 3.2.3).
-#' Thus the sample size dependend k-ht threshold is capped at 0.7.
+#' Thus the sample size dependent k-hat threshold is capped at 0.7.
 #' @param S sample size
 #' @param ... unused
 #' @return threshold

R/importance_sampling.R

@@ -114,7 +114,7 @@ assert_importance_sampling_method_is_implemented <- function(x){
     stop("Importance sampling method '",
          x,
          "' is not implemented. Implemented methods: '",
-         paste0(implemented_is_methods, collapse = "', '"),
+         paste0(implemented_is_methods(), collapse = "', '"),
          "'")
   }
 }

R/loo-glossary.R

@@ -7,7 +7,7 @@
 #' @template bayesvis-reference
 #'
 #' @description
-#'   The pages provides definitions to key terms. Also see the
+#'   This page provides definitions of key terms. Also see the
 #'   [FAQ page](https://mc-stan.org/loo/articles/online-only/faq.html) on
 #'   the __loo__ website for answers to frequently asked questions.
 #'

R/loo-package.R

@@ -82,7 +82,7 @@
 #' <https://mc-stan.org>.
 #'
 #' Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and
-#' widely application information criterion in singular learning theory.
+#' widely applicable information criterion in singular learning theory.
 #' *Journal of Machine Learning Research* **11**, 3571-3594.
 #'
 #' Zhang, J., and Stephens, M. A. (2009). A new and efficient estimation method

R/loo_compare.psis_loo_ss_list.R

@@ -31,7 +31,7 @@ loo_compare.psis_loo_ss_list <- function(x, ...) {
   return(comp)
 }
 
-#' Compare a reference loo object with a comaprison loo object
+#' Compare a reference loo object with a comparison loo object
 #' @noRd
 #' @param ref_loo A named list with a `psis_loo_ss` object.
 #' @param compare_loo A named list with a  `psis_loo_ss` object.

R/loo_model_weights.R

@@ -7,7 +7,7 @@
 #'
 #' @export
 #' @param x A list of `"psis_loo"` objects (objects returned by [loo()]) or
-#'   pointwise log-likelihood matrices or , one for each model. If the list
+#'   pointwise log-likelihood matrices, one for each model. If the list
 #'   elements are named the names will be used to label the models in the
 #'   results. Each matrix/object should have dimensions \eqn{S} by \eqn{N},
 #'   where \eqn{S} is the size of the posterior sample (with all chains merged)

R/loo_subsample.R

@@ -417,7 +417,7 @@ update.psis_loo_ss <- function(object, ...,
 #' @param rep If sampling with replacement is used, an observation can have
 #'   multiple samples and these are then repeated in the returned object if
 #'   `rep=TRUE` (e.g., a vector `c(1,1,2)` indicates that observation 1 has been
-#'   subampled two times). If `rep=FALSE` only the unique indices are returned.
+#'   subsampled two times). If `rep=FALSE` only the unique indices are returned.
 #'
 #' @return An integer vector.
 #'

R/split_moment_matching.R

@@ -21,7 +21,7 @@
 #'   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 observation based
+#'   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 relative effective sample size of the `i`'th log
 #'   likelihood draws.

R/waic.R

@@ -34,7 +34,7 @@
 #'
 #' @references
 #' Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and
-#' widely application information criterion in singular learning theory.
+#' widely applicable information criterion in singular learning theory.
 #' *Journal of Machine Learning Research* **11**, 3571-3594.
 #'
 #' @template loo-and-psis-references