A set of functions for reproducible and shareable analysis of models from an online repository, comparing and diagnostic models. The package has been developed specifically to improve reproducibility and comparability of SDMs in R by allowing users to encode entire SDM analyses as repeatable and extensible workflows consisting of independently executable, community-contributed modules. The module-workflow structure enables scientists to more easily create and share components of their analysis, and then, access, modify, reuse and combine the components of others.

The packages provides a set of tools for post processing the outcomes of species distribution modeling exercises. It includes novel methods for comparing models and tracking changes in distributions through time. It further includes methods for visualizing outcomes, selecting thresholds, calculating measures of accuracy and landscape fragmentation statistics, etc. This package was made possible in part by financial support from the Australian Research Council & ARC Research Network for Earth System Science.

A set of tools for training, selecting, and evaluating maximum entropy (and standard logistic regression) distribution models. This package provides tools for user-controlled transformation of explanatory variables, selection of variables by nested model comparison, and flexible model evaluation and projection. It follows principles based on the maximum-likelihood interpretation of maximum entropy modeling, and uses infinitely-weighted logistic regression for model fitting.

Algorithms for presence-only (Bioclim, Domain, Mahalanobis distance), presence-background (Maxent), and presence-absence (BRT). The package includes functions for comparing models.

A set of functions to compute fuzzy versions of species occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis, and prevalence independent favorability), and pairwise fuzzy similarity. Moreover, it includes additional functions for model consensus and comparison, and for data preparation: unique abbreviations of species names, gridding (thinning) point occurrence data onto raster maps, converting species lists to presence-absence tables, transposing part of a data frame, selecting relevant variables for models, assessing the false discovery rate, or analysing and dealing with multicollinearity.