presence-background
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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.
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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) https://doi.org/10.1111/ecog.01881.