Ecological Niche Modeling using Presence-Absence Data

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Documentation for package ‘enmpa’ version 0.1.5

• DESCRIPTION file.
• Package NEWS.

Help Pages

aux_string_comb	Get GLM formulas according to defined response types
aux_var_comb	Get GLM formulas according to defined response types
calibration_glm	GLM calibration with presence-absence data
cal_res	Example of results obtained from GLM calibration using enmpa
enmpa	enmpa: Ecological Niche Modeling using Presence-Absence Data
enm_data	Example data used to run model calibration exercises
evaluation_stats	Summary of evaluation statistics for candidate models
fit_glms	Fitting selected GLMs models
fit_selected	Fitting selected GLMs models
get_formulas	Get GLM formulas according to defined response types
get_formulas_main	Get GLM formulas according to defined response types
independent_eval01	Evaluate final models using independent data
independent_eval1	Evaluate final models using independent data
kfold_partition	K-fold data partitioning
model_selection	Selection of best candidate models considering various criteria
model_validation	Model validation options
niche_signal	Niche Signal detection using one or multiple variables
niche_signal_permanova	Niche Signal detection using one or multiple variables
niche_signal_univariate	Niche Signal detection using one or multiple variables
optimize_metrics	Find threshold values to produce three optimal metrics
plot_importance	Plot variable importance
plot_niche_signal	Plot Niche Signal results
plot_niche_signal_permanova	Plot Niche Signal results
plot_niche_signal_univariate	Plot Niche Signal results
predict_glm	Extension of glm predict to generate predictions of different types
predict_selected	Predictions for the models selected after calibration
proc_enm	Partial ROC calculation
response_curve	Variable response curves for GLMs
sel_fit	Example of selected models fitted
test	Example data used to test models
var_importance	Variable importance for GLMs