Merge branch 'v205_cran'

Author: jamiemkass

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: ENMeval
 Type: Package
 Title: Automated Tuning and Evaluations of Ecological Niche Models
-Version: 2.0.5
-Date: 2024-12-14
+Version: 2.0.5.2
+Date: 2025-05-01
 Authors@R: c(
   person("Jamie M.","Kass", email = "[email protected]", role = c("aut","cre")), 
   person("Robert","Muscarella", role = "aut"),
@@ -14,9 +14,9 @@ Authors@R: c(
   person("Robert P.","Anderson", role = c("aut")))
 Maintainer: Jamie M. Kass <[email protected]>
 Description: Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: <https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0-vignette.html>.
-License: file LICENSE
+License: GPL-3
 Encoding: UTF-8
-Depends: methods, R (>= 3.5.0)
+Depends: methods, R (>= 4.1.0)
 Imports: 
     terra,
     maxnet,
@@ -49,5 +49,4 @@ Suggests:
     ecospat,
     geodata,
     usdm
-VignetteBuilder: knitr
 URL: https://jamiemkass.github.io/ENMeval/

ENMeval.Rproj

@@ -1,5 +1,5 @@
 Version: 1.0
-ProjectId: 9e305864-7f20-4c45-afc9-1414e3c271f9
+ProjectId: 906253b9-3779-4435-afd3-64b028824978
 
 RestoreWorkspace: Default
 SaveWorkspace: Default
@@ -16,6 +16,3 @@ LaTeX: pdfLaTeX
 BuildType: Package
 PackageUseDevtools: Yes
 PackageInstallArgs: --no-multiarch --with-keep.source
-PackageBuildArgs: --no-build-vignettes
-PackageBuildBinaryArgs: --no-build-vignettes
-PackageCheckArgs: --ignore-vignettes

LICENSE

@@ -1,3 +1,13 @@
+ENMeval 2.0.5.2
+===========
+o  Fixed an error in maxnet.predictRaster that resulted in ignoring categorical variables when making predictions. This happened because terra::values was converting the cateogrical values to numeric, and the maxnet model had them specified as categorical, so they were ignored. For maxent.jar models, they are specified internally as numeric, unlike for maxnet.
+o  Fixed a small error that resulted in conversion of categorical variable values to numeric for maxnet models for AICc calculation. This conversion was meant only for maxent.jar models, but it should not have affected AIC calculations.
+o  Added a note about the validation.bg option in the documentation for ENMevaluate.
+
+ENMeval 2.0.5.1
+===========
+o  Fixed a typo in maxnet.predictRaster.
+
 ENMeval 2.0.5
 ===========
 * This one is kind of a big update, making the switch to terra and predicts*

NEWS

@@ -87,7 +87,8 @@
 #' 1. By default, validation AUC is calculated with respect to the full background (training + validation).
 #' This approach follows Radosavljevic & Anderson (2014).This setting can be changed by assigning 
 #' other.settings$validation.bg to "partition", which will calculate AUC with respect 
-#' to the validation background only. The default value for other.settings$validation.bg is "full". 
+#' to the validation background only. The default value for other.settings$validation.bg is "full".
+
 #' NOTE: When examining validation AUC and other discrimination metrics, the "full" option will likely 
 #' result in higher performance than for the "partition" option because more and varied background data 
 #' should lead to higher discriminatory power for the model. Users should thus make sure they are correctly

R/ENMevaluate.R

@@ -61,9 +61,9 @@
 #' species distributions. \emph{Journal of Biogeography}, \bold{46}: 1101-1111. 
 #' \doi{10.1111/jbi.13573}
 #' 
-#' Kass, J. M., Anderson, R. P., Espinosa-Lucas, A., Juárez-Jaimes, V., 
-#' Martínez-Salas, E., Botello, F.,  Tavera, G., Flores-Martínez, J. J., & 
-#' Sánchez-Cordero, V. (2020). Biotic predictors with phenological information 
+#' Kass, J. M., Anderson, R. P., Espinosa-Lucas, A., Juarez-Jaimes, V., 
+#' Martinez-Salas, E., Botello, F.,  Tavera, G., Flores-Martinez, J. J., & 
+#' Sanchez-Cordero, V. (2020). Biotic predictors with phenological information 
 #' improve range estimates for migrating monarch butterflies in Mexico. 
 #' \emph{Ecography}, \bold{43}: 341-352. \doi{10.1111/ecog.04886}
 #' 

R/ENMnulls.R

@@ -56,7 +56,7 @@ NULL
 #' 
 #' Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. \emph{Environmental Conservation}, \bold{24}: 38-49. \doi{10.1017/S0376892997000088}
 #' 
-#' Jiménez‐Valverde, A. (2012). Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. \emph{Global Ecology and Biogeography}, \bold{21}: 498-507. \doi{10.1111/j.1466-8238.2011.00683.x}
+#' Jimenez-Valverde, A. (2012). Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. \emph{Global Ecology and Biogeography}, \bold{21}: 498-507. \doi{10.1111/j.1466-8238.2011.00683.x}
 #' 
 #' \emph{AUC diff}
 #' 

R/classes.R

@@ -48,7 +48,7 @@ maxnet.args <- function(occs.z, bg.z, tune.tbl.i, other.settings) {
 }
 
 maxnet.predict <- function(mod, envs, other.settings) {
-  requireNamespace("maxnet", quitely = TRUE)
+  requireNamespace("maxnet", quietly = TRUE)
   # function to generate a prediction Raster* when raster data is specified as envs,
   # and a prediction data frame when a data frame is specified as envs
   if(inherits(envs, "SpatRaster") == TRUE) {

R/enm.maxnet.R

@@ -51,7 +51,7 @@
 #' @references
 #' Hijmans, R. J. (2023). terra: Spatial Data Analysis. Available online at: \url{https://cran.r-project.org/package=terra}.
 #' 
-#' Muscarella, R., Galante, P. J., Soley‐Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M., & Anderson, R. P. (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. \emph{Methods in Ecology and Evolution}, 5(11), 1198-1205. \doi{https://doi.org/10.1111/2041-210X.12945} 
+#' Muscarella, R., Galante, P. J., Soley-Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M., & Anderson, R. P. (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. \emph{Methods in Ecology and Evolution}, 5(11), 1198-1205. \doi{10.1111/2041-210X.12945} 
 #' 
 #' Pearson, R. G., Raxworthy, C. J., Nakamura, M. and Peterson, A. T. (2007). Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. \emph{Journal of Biogeography}, \bold{34}: 102-117. \doi{10.1111/j.1365-2699.2006.01594.x}
 #' 

R/partitions.R

@@ -30,8 +30,9 @@
 #' @importFrom methods is
 #' @importFrom utils count.fields
 #' @importFrom stats setNames
-#' @export
+#' 
 #' @examples
+#' \dontrun{
 #' # Below we use the predicts::MaxEnt example to fit a model:
 #' library(predicts)
 #' occs <- read.csv(file.path(system.file(package="predicts"),
@@ -42,6 +43,8 @@
 #' lam <- parse_lambdas(me)
 #' lam
 #' str(lam, 1)
+#' }
+#' @export
 
 parse_lambdas <- function(lambdas) {
   if(methods::is(lambdas, 'MaxEnt_model')) {