From 1 - 7 / 7
  • A set of tools helping with the development of critical phases of the ecological niche modeling process in Maxent. Pre-modeling analyses and explorations can be done to prepare data. Model calibration (model selection) can be done by creating and testing several candidate models. Handy options for producing final models, evaluating such models, and assessing extrapolation risks are also included. Tools for post-modeling analyses are implemented to allow for further exploration of results.

  • 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.

  • The package 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. Moreover, it provides 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 niche overlap between model predictions, among others. The package was originally built for Maxent models, but the current version allows possible extensions for any modeling algorithm.

  • Algorithms for presence-only (SRE), presence-background (Maxent) and presence-absence (GAM, GLM, BRT, ANN). The package includes a set of functions for species distribution modeling, calibration and evaluation, ensemble of models, ensemble forecasting and visualisation. The package permits to run consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences) dataset and to combine them in ensemble models and ensemble projections. Some bench of other evaluation and visualisation tools are also available within the package.

  • A set of functions for performing variation partitioning, discrimination and calibration metrics, optimizing prediction thresholds, performing multivariate environmental similarity surface (MESS) analysis, and displaying analytical plots.

  • An extensible framework that includes algorithms for presence-background (Maxent) and presence-absence (GAM, GLM, MARS, SVM, RF) for developing species distribution models using individual and community-based approaches, generate ensembles of models, calibrate and evaluate the models, and predict species potential distributions in space and time. For more information, please check the paper Naimi, B., Araujo, M.B. (2016)

  • The package includes algorithms for presence-background (Maxent) and presence-absence (GAM, GLM, GBM, SVM, RF, ANN). Moreover, it contains functions for sampling bias correction, sampling pseudoabsences and background points, data partitioning, and reducing collinearity in predictors; fitting and evaluating models, ensembles of small models and ensemble models; models’ predictions, interpolation and overprediction correction.