Software
JIMÉNEZ-VALVERDE A. (2024) vandalico: Evaluation of Presence-Absence Models. R package version 0.1.0. Available at https://cran.r-project.org/web/packages/vandalico/index.html NEW VERSION
Collection of functions to evaluate presence-absence models. It comprises functions to adjust discrimination statistics for the representativeness effect through case-weighting, along with functions for visualizing the outcomes. Originally outlined in: Jiménez-Valverde (2022) The uniform AUC: dealing with the representativeness effect in presence-absence models. Methods Ecol. Evol, 13, 1224-1236.
COBOS, M.E., BARVE, V., BARVE, N., JIMÉNEZ-VALVERDE A. & NUÑEZ-PENICHET, C. (2021) rangemap: Simple Tools for Defining Species Ranges. R package version 0.1.18. Available at https://CRAN.R-project.org/package=rangemap
A collection of tools to create species range maps based on occurrence data, statistics, and spatial objects. Other tools in this collection can be used to analyze the environmental characteristics of the species ranges. Plotting options to represent results in various manners are also available. Results obtained using these tools can be used to explore the distribution of species and define areas of occupancy and extent of occurrence of species. Other packages help to explore species distributions using distinct methods, but options presented in this set of tools (e.g., using trend surface analysis and concave hull polygons) are exclusive. Description of methods, approaches, and comments for some of the tools implemented here can be found in: IUCN (2001) <https://portals.iucn.org/library/node/10315>, Peterson et al. (2011) <https://www.degruyter.com/princetonup/view/title/506966>, and Graham and Hijmans (2006) <doi:10.1111/j.1466-8238.2006.00257.x>.
BARBOSA A.M., BROWN J.A., JIMÉNEZ-VALVERDE A. & REAL R. (2024) modEvA: Model Evaluation and Analysis. R package version 3.23. Available at https://cran.r-project.org/web/packages/modEvA/index.html
Analyses species distribution models and evaluates their performance. It includes functions for variation partitioning, extracting variable importance, computing several metrics of model discrimination and calibration performance, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots. Initially described in Barbosa et al. (2013) <doi:10.1111/ddi.12100>.