small doc fixes

R/sample_pseudoabs_time.R

@@ -4,20 +4,18 @@
 #' of semantics) points from a raster given a set of presences.
 #' The locations returned as the center points of the sampled cells, which can
 #' not overlap with the presences. The following methods are implemented:
-#' {/itemize:
-#' /item: 'random': pseudo-absences/background randomly sampled from the region covered by the
+#' * 'random': pseudo-absences/background randomly sampled from the region covered by the
 #' raster (i.e. not NAs).
-#' /item: 'dist_min': pseudo-absences/background randomly sampled from the region excluding a buffer
+#' * 'dist_min': pseudo-absences/background randomly sampled from the region excluding a buffer
 #' of 'dist_min' from presences (distances in 'm' for lonlat rasters, and in map
 #' units for projected rasters).
-#' /item: 'dist_max': pseudo-absences/background randomly sampled from the unioned buffers
+#' * 'dist_max': pseudo-absences/background randomly sampled from the unioned buffers
 #' of 'dist_max' from presences (distances in 'm' for lonlat rasters, and in map
 #' units for projected rasters). Using the union of buffers means that areas that
 #' are in multiple buffers are not oversampled. This is also referred to as "thickening".
-#' /item: 'dist_disc': pseudo-absences/background randomly sampled from the unioned discs around presences
+#' * 'dist_disc': pseudo-absences/background randomly sampled from the unioned discs around presences
 #' with the two values of 'dist_disc' defining the minimum and maximum distance from
 #' presences.
-#' }
 #' @param data An [`sf::sf`] data frame, or a data frame with coordinate variables.
 #' These can be defined in `coords`, unless they have standard names
 #' (see details below).

R/sdm_spec_boost_tree.R

@@ -6,16 +6,14 @@
 #' @param ... parameters to be passed to [parsnip::boost_tree()] to
 #' customise the model. See the help of that function for details.
 #' @param tune character defining the tuning strategy. Valid strategies are:
-#' {itemize:
-#' /item: "sdm" chooses hyperparameters that are most important to tune for
+#' * "sdm" chooses hyperparameters that are most important to tune for
 #' an sdm (for *boost_tree*: 'mtry', 'trees', 'tree_depth', 'learn_rate',
 #' 'loss_reduction', and 'stop_iter')
-#' /item: "all" tunes all hyperparameters (for *boost_tree*: 'mtry', 'trees',
+#' * "all" tunes all hyperparameters (for *boost_tree*: 'mtry', 'trees',
 #' 'tree_depth', 'learn_rate',
 #' 'loss_reduction', 'stop_iter','min_n' and 'sample_size')
-#' /item: "custom" passes the options from '...'
-#' /item: "none" does not tune any hyperparameter
-#' }
+#' * "custom" passes the options from '...'
+#' * "none" does not tune any hyperparameter
 #' @returns a [parsnip::model_spec] of the model.
 #' @examples
 #' standard_bt_spec <- sdm_spec_boost_tree()

R/sdm_spec_maxent.R

@@ -7,13 +7,11 @@
 #' @param ... parameters to be passed to [maxent()] to
 #' customise the model. See the help of that function for details.
 #' @param tune character defining the tuning strategy. Valid strategies are:
-#' {itemize:
-#' /item: "sdm" chooses hyperparameters that are most important to tune for
+#' * "sdm" chooses hyperparameters that are most important to tune for
 #' an sdm (for *maxent*, 'mtry')
-#' /item: "all" tunes all hyperparameters (for *maxent*, 'mtry', 'trees' and 'min')
-#' /item: "custom" passes the options from '...'
-#' /item: "none" does not tune any hyperparameter
-#' }
+#' * "all" tunes all hyperparameters (for *maxent*, 'mtry', 'trees' and 'min')
+#' * "custom" passes the options from '...'
+#' * "none" does not tune any hyperparameter
 #' @returns a [parsnip::model_spec] of the model.
 #' @examples
 #' test_maxent_spec <- sdm_spec_maxent(tune = "sdm")

R/sdm_spec_rand_forest.R

@@ -9,13 +9,11 @@
 #' @param ... parameters to be passed to [parsnip::rand_forest()] to
 #' customise the model. See the help of that function for details.
 #' @param tune character defining the tuning strategy. Valid strategies are:
-#' {itemize:
-#' /item: "sdm" chooses hyperparameters that are most important to tune for
+#' * "sdm" chooses hyperparameters that are most important to tune for
 #' an sdm (for *rf*, 'mtry')
-#' /item: "all" tunes all hyperparameters (for *rf*, 'mtry', 'trees' and 'min')
-#' /item: "custom" passes the options from '...'
-#' /item: "none" does not tune any hyperparameter
-#' }
+#' * "all" tunes all hyperparameters (for *rf*, 'mtry', 'trees' and 'min')
+#' * "custom" passes the options from '...'
+#' * "none" does not tune any hyperparameter
 #' @returns a [parsnip::model_spec] of the model.
 #' @examples
 #' test_rf_spec <- sdm_spec_rf(tune = "sdm")

R/simple_ensemble.R

@@ -9,13 +9,11 @@
 #' @param ... not used, this function just creates an empty `simple_ensemble`
 #' object. Members are added with `add_best_candidates()`
 #' @returns an empty `simple_ensemble`. This is a tibble with columns:
-#' {/itemize:
-#' /item: `wflow_id`: the name of the workflows for which the best model was
+#' * `wflow_id`: the name of the workflows for which the best model was
 #' chosen
-#' /item: `workflow`: the trained workflow objects
-#' /item: `metrics`: metrics based on the crossvalidation resampling used
+#' * `workflow`: the trained workflow objects
+#' * `metrics`: metrics based on the crossvalidation resampling used
 #' to tune the models
-#' }
 #' @export
 
 simple_ensemble <- function(...) {

cran_comments.md → cran-comments.md

@@ -2,13 +2,14 @@ This is a new package not present on CRAN yet.
 
 On all testing environment (see below), we only get a note because of the
 package being a new submission, and a false positive for mispelling 
-(paleoclimate is an existing word and is spelled correctly).
+(author name in the reference, and tidymodels).
 
 ## Test environments
 - R-hub windows-x86_64-devel (r-devel)
 - R-hub ubuntu-gcc-release (r-release)
 - R-hub fedora-clang-devel (r-devel)
 - devtools::check_mac_release
+- devtools::check_win_devel()
 
 ## R CMD check results
 On all systems:

inst/WORDLIST

@@ -24,6 +24,7 @@ Schmiegelow
 SpatRaster
 StackOverflow
 TSS
+Tidymodels
 Trang
 WGS
 WorldClim