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

This service aims at running a species distribution modelling (sdm) service based on Maximum Entropy (maxent) algorithm. It is intended to predict the potential geographic distribution of a species based on environmental variables. Maxent algorithm is a probabilistic modelling technique used for making predictions based on incomplete information or limited data. It is based on the principle of maximum entropy, which states that when making predictions, one should choose the probability distribution that is the least informative, or maximally uncertain, while still satisfying a set of known constraints. The application calculates the distribution model of a species by taking as input files a vector of presence and a group of raster files that represent the background or environmental layers and providing five output files: elapid_object.ela, maxent_prediction_model.tif, maxent_prediction_plot.png, dependency_plots.png, and auc_score.txt. The application can take as input also a set of parameters (optional), which have default values in case the user does not want to configure them.