update references

Author: HannaMeyer

CAST.Rproj

@@ -1,5 +1,4 @@
 Version: 1.0
-ProjectId: ee3f8bce-db78-4d8f-b306-4ac0b8bf412d
 
 RestoreWorkspace: Default
 SaveWorkspace: Default

R/CAST-package.R

@@ -14,9 +14,10 @@
 #' @author Hanna Meyer, Carles Milà, Marvin Ludwig, Jan Linnenbrink, Fabian Schumacher
 #' @references
 #' \itemize{
-#' \item Meyer, H., Ludwig, L., Milà, C., Linnenbrink, J., Schumacher, F. (2024): The CAST package for training and assessment of spatial prediction models in R. arXiv, https://doi.org/10.48550/arXiv.2404.06978.
-#' \item Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H.: kNNDM: k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1308, 2023.
-#' \item Milà, C., Mateu, J., Pebesma, E., Meyer, H. (2022): Nearest Neighbour Distance Matching Leave-One-Out Cross-Validation for map validation. Methods in Ecology and Evolution 00, 1– 13.
+#' \item Meyer, H., Ludwig, L., Milà, C., Linnenbrink, J., Schumacher, F. (2026): The CAST package for training and assessment of spatial prediction models in R. In: Rocchini, D. (eds) R Coding for Ecology. Use R!. Springer, Cham.
+#' \item Schumacher, F., Knoth, C., Ludwig, M., Meyer, H. (2025): Estimation of local training data point densities to support the assessment of spatial prediction uncertainty. Geosci. Model Dev., 18, 10185–10202.
+#' \item Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H. (2024): kNNDM: k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation, Geosci. Model Dev., 17, 5897–5912.
+#' \item Milà, C., Mateu, J., Pebesma, E., Meyer, H. (2022): Nearest Neighbour Distance Matching Leave-One-Out Cross-Validation for map validation. Methods in Ecology and Evolution 13, 1304– 1316.
 #' \item Meyer, H., Pebesma, E. (2022): Machine learning-based global maps of ecological variables and the challenge of assessing them. Nature Communications. 13.
 #' \item Meyer, H., Pebesma, E. (2021): Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution. 12, 1620– 1633.
 #' \item Meyer, H., Reudenbach, C., Wöllauer, S., Nauss, T. (2019): Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction. Ecological Modelling. 411, 108815.

R/aoa.R

@@ -59,9 +59,9 @@
 #' Estimating the area of applicability of spatial prediction models.
 #' Methods in Ecology and Evolution 12: 1620-1633. \doi{10.1111/2041-210X.13650}
 #'
-#' Schumacher, F., Knoth, C., Ludwig, M., Meyer, H. (2024):
+#' Schumacher, F., Knoth, C., Ludwig, M., Meyer, H. (2025):
 #' Estimation of local training data point densities to support the assessment
-#' of spatial prediction uncertainty. EGUsphere. \doi{10.5194/egusphere-2024-2730}.
+#' of spatial prediction uncertainty. Geosci. Model Dev., 18, 10185–10202. \doi{10.5194/gmd-18-10185-2025}
 #'
 #' @seealso \code{\link{trainDI}}, \code{\link{normalize_DI}}, \code{\link{errorProfiles}}
 #' @examples

R/knndm.R

@@ -74,8 +74,8 @@
 #' For spatial visualization of fold affiliation see examples.
 #' @references
 #' \itemize{
-#' \item Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H.: kNNDM: k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1308, 2023.
-#' \item Milà, C., Mateu, J., Pebesma, E., Meyer, H. (2022): Nearest Neighbour Distance Matching Leave-One-Out Cross-Validation for map validation. Methods in Ecology and Evolution 00, 1– 13.
+#' \item Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H. (2024): kNNDM: k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation. Geosci. Model Dev., 17, 5897–5912. https://doi.org/10.5194/gmd-17-5897-2024.
+#' \item Milà, C., Mateu, J., Pebesma, E., Meyer, H. (2022): Nearest Neighbour Distance Matching Leave-One-Out Cross-Validation for map validation. Methods in Ecology and Evolution 13, 1304– 1316. https://doi.org/10.1111/2041-210X.13851.
 #' }
 #' @seealso \code{\link{geodist}}, \code{\link{nndm}}
 #'

README.md

@@ -26,7 +26,7 @@ https://hannameyer.github.io/CAST/
 
 ## Tutorials
 
-* [The CAST package for training and assessment of spatial prediction models in R](https://arxiv.org/abs/2404.06978)
+* [Book chapter:  The CAST package for training and assessment of spatial prediction models in R](https://doi.org/10.1007/978-3-031-99665-8_11). [Preprint](https://arxiv.org/abs/2404.06978)
 
 * [Introduction to CAST](https://hannameyer.github.io/CAST/articles/cast01-CAST-intro.html)
 
@@ -48,14 +48,12 @@ https://www.youtube.com/watch?v=mkHlmYEzsVQ.
 
 ## Scientific documentation of the methods
 
-* Meyer, H., Ludwig, L., Milà, C., Linnenbrink, J., Schumacher, F. (2024): The CAST package for training and assessment of spatial prediction models in R. arXiv, https://doi.org/10.48550/arXiv.2404.06978.
+* Meyer, H., Ludwig, M., Milà, C., Linnenbrink, J., Schumacher, F. (2026). The CAST Package for Training and Assessment of Spatial Prediction Models. In: Rocchini, D. (eds) R Coding for Ecology. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-031-99665-8_11. Preprint: https://doi.org/10.48550/arXiv.2404.06978.
 
 ### Spatial cross-validation
-* Milà, C., Mateu, J., Pebesma, E., Meyer, H. (2022): Nearest Neighbour Distance Matching Leave-One-Out Cross-Validation for map validation. Methods in Ecology and Evolution 00, 1– 13.
-https://doi.org/10.1111/2041-210X.13851
+* Milà, C., Mateu, J., Pebesma, E., Meyer, H. (2022): Nearest Neighbour Distance Matching Leave-One-Out Cross-Validation for map validation. Methods in Ecology and Evolution 13, 1304– 1316. https://doi.org/10.1111/2041-210X.13851
 
-* Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H.: kNNDM (2023): k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation. EGUsphere [preprint]. 
-https://doi.org/10.5194/egusphere-2023-1308
+* Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H.: kNNDM (2024): k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation. Geosci. Model Dev., 17, 5897–5912. https://doi.org/10.5194/gmd-17-5897-2024.
 
 * Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauss, T. (2018): Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001
 
@@ -67,11 +65,11 @@ https://doi.org/10.5194/egusphere-2023-1308
 ### Area of applicability
 * Meyer, H., Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12, 1620– 1633. https://doi.org/10.1111/2041-210X.13650 
 
-* Schumacher, F., Knoth, C., Ludwig, M., Meyer, H. (2024): Estimation of local training data point densities to support the assessment of spatial prediction uncertainty. EGUsphere. https://doi.org/10.5194/egusphere-2024-2730.
+* Schumacher, F., Knoth, C., Ludwig, M., Meyer, H. (2025): Estimation of local training data point densities to support the assessment of spatial prediction uncertainty. Geosci. Model Dev., 18, 10185–10202. https://doi.org/10.5194/gmd-18-10185-2025.
 
 ### Applications and use cases
 * Meyer, H., Pebesma, E. (2022): Machine learning-based global maps of ecological variables and the challenge of assessing them. Nature Communications, 13. https://www.nature.com/articles/s41467-022-29838-9
 
-* Ludwig, M., Moreno-Martinez, A., Hoelzel, N., Pebesma, E., Meyer, H. (2023): Assessing and improving the transferability of current global spatial prediction models. Global Ecology and Biogeography.  https://doi.org/10.1111/geb.13635.
+* Ludwig, M., Moreno-Martinez, A., Hoelzel, N., Pebesma, E., Meyer, H. (2023): Assessing and improving the transferability of current global spatial prediction models. Global Ecology and Biogeography, 32, 356–368. https://doi.org/10.1111/geb.13635.
 
-* Milà, C., Ludwig, M., Pebesma, E., Tonne, C., and Meyer, H.: Random forests with spatial proxies for environmental modelling: opportunities and pitfalls, EGUsphere [preprint]. https://doi.org/10.5194/egusphere-2024-138, 2024. 
+* Milà, C., Ludwig, M., Pebesma, E., Tonne, C., and Meyer, H. (2024): Random forests with spatial proxies for environmental modelling: opportunities and pitfalls. Geosci. Model Dev., 17, 6007–6033. https://doi.org/10.5194/gmd-17-6007-2024.