diff --git a/.gitattributes b/.gitattributes
index fce2bdcc..5046e422 100644
--- a/.gitattributes
+++ b/.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
+
diff --git a/R/diagnostics.R b/R/diagnostics.R
index 77b4eb43..3d12bdb4 100644
--- a/R/diagnostics.R
+++ b/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
diff --git a/R/importance_sampling.R b/R/importance_sampling.R
index 80ec5c8a..f8dfd4d3 100644
--- a/R/importance_sampling.R
+++ b/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 = "', '"),
          "'")
   }
 }
diff --git a/R/loo-glossary.R b/R/loo-glossary.R
index 06ba5ce6..804bd58c 100644
--- a/R/loo-glossary.R
+++ b/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.
 #'
diff --git a/R/loo-package.R b/R/loo-package.R
index 12a39200..70e58047 100644
--- a/R/loo-package.R
+++ b/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
diff --git a/R/loo_compare.psis_loo_ss_list.R b/R/loo_compare.psis_loo_ss_list.R
index acd0690b..626f7428 100644
--- a/R/loo_compare.psis_loo_ss_list.R
+++ b/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.
diff --git a/R/loo_model_weights.R b/R/loo_model_weights.R
index 449e8feb..946dc7c3 100644
--- a/R/loo_model_weights.R
+++ b/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)
diff --git a/R/loo_subsample.R b/R/loo_subsample.R
index a12e8a79..cfff0999 100644
--- a/R/loo_subsample.R
+++ b/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.
 #'
diff --git a/R/split_moment_matching.R b/R/split_moment_matching.R
index b7bdb111..8ae5c91f 100644
--- a/R/split_moment_matching.R
+++ b/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.
diff --git a/R/waic.R b/R/waic.R
index b225ba7e..2dac6456 100644
--- a/R/waic.R
+++ b/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
diff --git a/man/loo-glossary.Rd b/man/loo-glossary.Rd
index 7aa19639..250f2e29 100644
--- a/man/loo-glossary.Rd
+++ b/man/loo-glossary.Rd
@@ -4,7 +4,7 @@
 \alias{loo-glossary}
 \title{LOO package glossary}
 \description{
-The pages provides definitions to key terms. Also see the
+This page provides definitions of key terms. Also see the
 \href{https://mc-stan.org/loo/articles/online-only/faq.html}{FAQ page} on
 the \strong{loo} website for answers to frequently asked questions.
 
diff --git a/man/loo-package.Rd b/man/loo-package.Rd
index e126f4a3..3b77ed7c 100644
--- a/man/loo-package.Rd
+++ b/man/loo-package.Rd
@@ -101,7 +101,7 @@ Stan Development Team (2018). RStan: the R interface to Stan, Version 2.17.3.
 \url{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.
 \emph{Journal of Machine Learning Research} \strong{11}, 3571-3594.
 
 Zhang, J., and Stephens, M. A. (2009). A new and efficient estimation method
diff --git a/man/loo_model_weights.Rd b/man/loo_model_weights.Rd
index fbcba94c..e7323912 100644
--- a/man/loo_model_weights.Rd
+++ b/man/loo_model_weights.Rd
@@ -28,7 +28,7 @@ pseudobma_weights(lpd_point, BB = TRUE, BB_n = 1000, alpha = 1)
 }
 \arguments{
 \item{x}{A list of \code{"psis_loo"} objects (objects returned by \code{\link[=loo]{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)
diff --git a/man/loo_moment_match_split.Rd b/man/loo_moment_match_split.Rd
index db69ad6f..91a6d826 100644
--- a/man/loo_moment_match_split.Rd
+++ b/man/loo_moment_match_split.Rd
@@ -46,7 +46,7 @@ returns a matrix of log-posterior density values of the unconstrained
 posterior draws passed via \code{upars}.}
 
 \item{log_lik_i_upars}{A function that takes arguments \code{x}, \code{upars}, and \code{i}
-and returns a vector of log-likeliood draws of the \code{i}th observation based
+and returns a vector of log-likelihood draws of the \code{i}th observation based
 on the unconstrained posterior draws passed via \code{upars}.}
 
 \item{r_eff_i}{MCMC relative effective sample size of the \code{i}'th log
diff --git a/man/obs_idx.Rd b/man/obs_idx.Rd
index da349e7e..d087f64f 100644
--- a/man/obs_idx.Rd
+++ b/man/obs_idx.Rd
@@ -12,7 +12,7 @@ obs_idx(x, rep = TRUE)
 \item{rep}{If sampling with replacement is used, an observation can have
 multiple samples and these are then repeated in the returned object if
 \code{rep=TRUE} (e.g., a vector \code{c(1,1,2)} indicates that observation 1 has been
-subampled two times). If \code{rep=FALSE} only the unique indices are returned.}
+subsampled two times). If \code{rep=FALSE} only the unique indices are returned.}
 }
 \value{
 An integer vector.
diff --git a/man/pareto-k-diagnostic.Rd b/man/pareto-k-diagnostic.Rd
index bb659f79..fcb2db6f 100644
--- a/man/pareto-k-diagnostic.Rd
+++ b/man/pareto-k-diagnostic.Rd
@@ -45,7 +45,7 @@ mcse_loo(x, threshold = NULL)
 \item{x}{An object created by \code{\link[=loo]{loo()}} or \code{\link[=psis]{psis()}}.}
 
 \item{threshold}{For \code{pareto_k_ids()}, \code{threshold} is the minimum \eqn{k}
-value to flag (default is a sample size \code{S} dependend threshold
+value to flag (default is a sample size \code{S} dependent threshold
 \code{1 - 1 / log10(S)}). For \code{mcse_loo()}, if any \eqn{k} estimates are
 greater than \code{threshold} the MCSE estimate is returned as \code{NA}
 See \strong{Details} for the motivation behind these defaults.}
diff --git a/man/waic.Rd b/man/waic.Rd
index 73db9d8f..345bd638 100644
--- a/man/waic.Rd
+++ b/man/waic.Rd
@@ -116,7 +116,7 @@ print(compare(waic1, waic2), digits = 2)
 }
 \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.
 \emph{Journal of Machine Learning Research} \strong{11}, 3571-3594.
 
 Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model