Package: enmpa 0.1.9

enmpa: Ecological Niche Modeling using Presence-Absence Data

A set of tools to perform Ecological Niche Modeling with presence-absence data. It includes algorithms for data partitioning, model fitting, calibration, evaluation, selection, and prediction. Other functions help to explore signals of ecological niche using univariate and multivariate analyses, and model features such as variable response curves and variable importance. Unique characteristics of this package are the ability to exclude models with concave quadratic responses, and the option to clamp model predictions to specific variables. These tools are implemented following principles proposed in Cobos et al., (2022) <doi:10.17161/bi.v17i.15985>, Cobos et al., (2019) <doi:10.7717/peerj.6281>, and Peterson et al., (2008) <doi:10.1016/j.ecolmodel.2007.11.008>.

Authors:Luis F. Arias-Giraldo [aut, cre], Marlon E. Cobos [aut], A. Townsend Peterson [ctb]

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enmpa.pdf |enmpa.html
enmpa/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/luisagi/enmpa/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • cal_res - Example of results obtained from GLM calibration using enmpa
  • enm_data - Example data used to run model calibration exercises
  • sel_fit - Example of selected models fitted
  • test - Example data used to test models

On CRAN:

4.45 score 4 stars 5 scripts 516 downloads 31 exports 16 dependencies

Last updated 3 months agofrom:514f7e6705. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 13 2024
R-4.5-win-x86_64OKOct 13 2024
R-4.5-linux-x86_64OKOct 13 2024
R-4.4-win-x86_64OKOct 13 2024
R-4.4-mac-x86_64OKOct 13 2024
R-4.4-mac-aarch64OKOct 13 2024
R-4.3-win-x86_64OKOct 13 2024
R-4.3-mac-x86_64OKOct 13 2024
R-4.3-mac-aarch64OKOct 13 2024

Exports:aux_string_combaux_var_combcalibration_glmevaluation_statsfit_glmsfit_selectedget_formulasget_formulas_mainindependent_eval01independent_eval1jackknifekfold_partitionmodel_selectionmodel_validationnew_enmpa_calibrationnew_enmpa_fitted_modelsniche_signalniche_signal_permanovaniche_signal_univariateoptimize_metricsplot_importanceplot_jkplot_niche_signalplot_niche_signal_permanovaplot_niche_signal_univariatepredict_glmpredict_selectedproc_enmresp2varresponse_curvevar_importance

Dependencies:clustercodetoolsdoSNOWellipseforeachiteratorslatticeMASSMatrixmgcvnlmepermuteRcppsnowterravegan

Readme and manuals

Help Manual

Help pageTopics
Example of results obtained from GLM calibration using enmpacal_res
GLM calibration with presence-absence datacalibration_glm
Example data used to run model calibration exercisesenm_data
enmpa: Ecological Niche Modeling using Presence-Absence Dataenmpa-package enmpa
Constructor of S3 objects of class enmpa_calibrationenmpa_calibration new_enmpa_calibration
Constructor of S3 objects of class enmpa_fitted_modelsenmpa_fitted_models new_enmpa_fitted_models
Summary of evaluation statistics for candidate modelsevaluation_stats
Fitting selected GLMs modelsfit_glms fit_selected
Get GLM formulas according to defined response typesaux_string_comb aux_var_comb get_formulas get_formulas_main
Evaluate final models using independent dataindependent_eval01 independent_eval1
Jackkniffe test for variable contributionjackknife
K-fold data partitioningkfold_partition
Selection of best candidate models considering various criteriamodel_selection
Model validation optionsmodel_validation
Niche Signal detection using one or multiple variablesniche_signal niche_signal_permanova niche_signal_univariate
Find threshold values to produce three optimal metricsoptimize_metrics
Plot variable importanceplot_importance
Jackkniffe plot for variable contributionplot_jk
Plot Niche Signal resultsplot_niche_signal plot_niche_signal_permanova plot_niche_signal_univariate
Extension of glm predict to generate predictions of different typespredict_glm
Predictions for the models selected after calibrationpredict_selected
Print a short version of elements in 'calibration' and 'fitted models' objectsprint print,enmpa_calibration-method print,enmpa_fitted_models-method print.enmpa_calibration print.enmpa_fitted_models
Partial ROC calculationproc_enm
Two-Way interaction response plotresp2var
Variable response curves for GLMsresponse_curve
Example of selected models fittedsel_fit
Summary of 'calibration' and 'fitted models'summary summary,enmpa_calibration-method summary,enmpa_fitted_models-method summary.enmpa_calibration summary.enmpa_fitted_models
Example data used to test modelstest
Variable importance for GLMsvar_importance