MaxentVariableSelection: Selecting the Best Set of Relevant Environmental Variables along with the Optimal Regularization Multiplier for Maxent Niche Modeling

Complex niche models show low performance in identifying the most important range-limiting environmental variables and in transferring habitat suitability to novel environmental conditions (Warren and Seifert, 2011 <doi:10.1890/10-1171.1>; Warren et al., 2014 <doi:10.1111/ddi.12160>). This package helps to identify the most important set of uncorrelated variables and to fine-tune Maxent's regularization multiplier. In combination, this allows to constrain complexity and increase performance of Maxent niche models (assessed by information criteria, such as AICc (Akaike, 1974 <doi:10.1109/TAC.1974.1100705>), and by the area under the receiver operating characteristic (AUC) (Fielding and Bell, 1997 <doi:10.1017/S0376892997000088>). Users of this package should be familiar with Maxent niche modelling.

Version: 1.0-3
Depends: R (≥ 3.1.2)
Imports: ggplot2, raster
Suggests: knitr, rmarkdown
Published: 2018-01-23
Author: Alexander Jueterbock
Maintainer: "Alexander Jueterbock" <Alexander-Jueterbock at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
SystemRequirements: maxent.jar file
Citation: MaxentVariableSelection citation info
Materials: README NEWS
CRAN checks: MaxentVariableSelection results


Reference manual: MaxentVariableSelection.pdf
Vignettes: 'MaxentVariableSelection' vignette
Package source: MaxentVariableSelection_1.0-3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: MaxentVariableSelection_1.0-3.tgz, r-oldrel: MaxentVariableSelection_1.0-3.tgz
Old sources: MaxentVariableSelection archive


Please use the canonical form to link to this page.