MIAmaxent R package - A Modular, Integrated Approach to Maximum Entropy Distribution Modeling

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.

Default

Date ( Publication)
2020-12-01
Status
Completed
Creator
  Department of Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway - Julien Vollering

Author
  Department of Research and Collections, Natural History Museum, University of Oslo, Oslo, Norway - Sabrina Mazzoni

Author
  Department of Research and Collections, Natural History Museum, University of Oslo, Oslo, Norway - Rune Halvorse

Author
  Department of Research and Collections, Natural History Museum, University of Oslo, Oslo, Norway - Steven Phillips

Keywords

modelling
Keywords

maximum entropy
Keywords

distribution models
Keywords

model comparison
Keywords

model evaluation
Keywords

R
Access constraints
License
Use limitation

https://cran.r-project.org/web/licenses/MIT

Other constraints

YEAR: 2016-2018

COPYRIGHT HOLDER: Julien Vollering, Sabrina Mazzoni, Rune Halvorsen, Steven Phillips
OnLine resource
CRAN link (

WWW:LINK-1.0-http--link

 )
Required Services

R (≥ 2.10)
Topic Category
Species distribution
Service Category

data collection and preparation
Service Category

data processing
Service Language
eng
Service Training

https://onlinelibrary.wiley.com/doi/10.1002/ece3.5654

Service User Manual

https://cran.r-project.org/web/packages/MIAmaxent/MIAmaxent.pdf

Metadata

File identifier
0e5951c5-adc1-48f3-ac4b-1f8256a0da74 XML
Metadata language
en
Hierarchy level
Service
Metadata Schema Version

1.2.0
 
 

Overviews

overview
modelling.jpg

Spatial extent

Keywords



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