A Modular, Integrated Approach to Maximum Entropy Distribution Modeling
Calculates variable contributions as RVA
Trains a model containing the explanatory variables specified.
Derive variables by transformation.
MIAmaxent: A Modular, Integrated Approach to Maximum Entropy Distribut...
Maxent model from .lambdas file.
Plot Frequency of Observed Presence (FOP).
Plot model response.
Predict method for infinitely-weighted logistic regression
Project model across explanatory data.
Read in data object from files.
Select parsimonious sets of derived variables.
Select parsimonious set of explanatory variables.
Calculate model AUC with test data.
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 is described in Vollering et al. (2019; <doi:10.1002/ece3.5654>).
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