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enmpa
View on CRAN: Click
here
Download and install enmpa package within the R console
Install from CRAN:
install.packages("enmpa")
Install from Github:
library("remotes")
install_github("cran/enmpa") Install by package version:
library("remotes")
install_version("enmpa", "0.2.5") Attach the package and use:
library("enmpa")
Maintained by
Luis F. Arias-Giraldo
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-12-01
Latest Update: 2025-06-12
Description:
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) ,
Cobos et al., (2019) ,
and Peterson et al., (2008) .
How to cite:
Luis F. Arias-Giraldo (2023). enmpa: Ecological Niche Modeling using Presence-Absence Data. R package version 0.2.5, https://cran.r-project.org/web/packages/enmpa. Accessed 07 Oct. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for enmpa
Functions, R codes and Examples using
the enmpa R package
Some associated functions: cal_res . calibration_glm . enm_data . enmpa . evaluation_stats . fit_glms . get_formulas . independent_eval . kfold_partition . model_selection . model_validation . niche_signal . optimize_metrics . plot_importance . plot_niche_signal . predict_glm . predict_selected . proc_enm . response_curve . sel_fit . test . var_importance .
Some associated R codes: RcppExports.R . calibration_glm.R . data_documentation.R . enmpa.R . evaluation_stats.R . fit_glm.R . get_formulas.R . independent_eval.R . kfold_partition.R . model_selection.R . model_validation.R . niche_signal.R . optimize_metrics.R . plot_importance.R . plot_niche_signal.R . predict_glm.R . predict_selected.R . proc.R . response_curve.R . selection_helpers.R . short_helpers.R . var_importance.R . Full enmpa package functions and examples
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