Package: MIAmaxent 1.4.1.9000

MIAmaxent: A Modular, Integrated Approach to Maximum Entropy Distribution Modeling

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>).

Authors:Julien Vollering [aut, cre], Sabrina Mazzoni [aut], Rune Halvorsen [aut], Steven Phillips [cph], Michael Bedward [ctb]

MIAmaxent_1.4.1.9000.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MIAmaxent/json (API)

# Install 'MIAmaxent' in R:
install.packages('MIAmaxent', repos = c('https://julienvollering.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/julienvollering/miamaxent/issues

Datasets:
  • toydata_dvs - Derived variables and transformation functions, from toy data.
  • toydata_seldvs - Selected derived variables accompanied by selection trails, from toy data.
  • toydata_selevs - Selected explanatory variables accompanied by selection trails, from toy data.
  • toydata_sp1po - Occurrence and environmental toy data.

On CRAN:

Conda:

6.75 score 13 stars 27 scripts 401 downloads 4 mentions 12 exports 21 dependencies

Last updated from:f518a7255f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK173
source / vignettesOK482
linux-release-x86_64OK203
macos-release-arm64OK193
macos-oldrel-arm64OK185
windows-develOK164
windows-releaseOK105
windows-oldrelOK88
wasm-releaseOK160

Exports:calculateRVAchooseModelderiveVarsmodelFromLambdasplotFOPplotRespplotResp2projectModelreadDataselectDVforEVselectEVtestAUC

Dependencies:classclidplyre1071genericsgluelifecyclemagrittrMASSpillarpkgconfigproxyR6Rcpprlangterratibbletidyselectutf8vctrswithr

Species Distribution Modeling with NCEAS Data
Introduction | Setup Swiss tree data | Examine occurrence--environment relationships | Transform and select environmental variables | Transform to derived variables | Selection stage 1: Select DVs for each EV | Selection stage 2: Select EVs for the final model | Explore the model with response curves | Evaluate the model with AUC | Summary and extended exercises | Advanced: The bias-variance tradeoff in model selection | Set up the alpha comparison | Fit and evaluate the models | Interpret results | Further reading | References
Rendered fromNCEAS.Rmdusingknitr::rmarkdown

Last update: 2025-12-08
Started: 2025-10-17

A modeling example
Introducing the data set | readData() | Examining patterns in occurrence | plotFOP() | Transforming explanatory variables (EVs) | deriveVars() | Selecting variables | selectDVforEV() | selectEV() | chooseModel() | Exploring the model | plotResp() | plotResp2() | calculateRVA() | Applying the model | projectModel() | Evaluating the model | testAUC() | Alternative: logistic regression | Acknowledgements | References
Rendered froma-modeling-example.Rmdusingknitr::rmarkdown

Last update: 2025-10-17
Started: 2018-02-26