Package: kuenm2 0.1.3

Weverton C. F. Trindade

kuenm2: Detailed Development of Ecological Niche Models

A new set of tools to help with the development of detailed ecological niche models using multiple algorithms. Pre-modeling analyses and explorations can be done to prepare data. Model calibration (model selection) can be done by creating and testing models with several parameter combinations. Handy options for producing final models with transfers are included. Other tools to assess extrapolation risks and variability in model transfers are also available. Methodological and theoretical basis for the methods implemented here can be found in: Peterson et al. (2011) <https://www.degruyter.com/princetonup/view/title/506966>, Radosavljevic and Anderson (2014) <doi:10.1111/jbi.12227>, Peterson et al. (2018) <doi:10.1111/nyas.13873>, Cobos et al. (2019) <doi:10.7717/peerj.6281>, Alkishe et al. (2020) <doi:10.1016/j.pecon.2020.03.002>, Machado-Stredel et al. (2021) <doi:10.21425/F5FBG48814>, Arias-Giraldo and Cobos (2024) <doi:10.17161/bi.v18i.21742>, Cobos et al. (2024) <doi:10.17161/bi.v18i.21742>.

Authors:Weverton C. F. Trindade [aut, cre], Luis F. Arias-Giraldo [aut], Luis Osorio-Olvera [aut], A. Townsend Peterson [aut], Marlon E. Cobos [aut]

kuenm2_0.1.3.tar.gz
kuenm2_0.1.3.zip(r-4.7)kuenm2_0.1.3.zip(r-4.6)kuenm2_0.1.3.zip(r-4.5)
kuenm2_0.1.3.tgz(r-4.6-any)kuenm2_0.1.3.tgz(r-4.5-any)
kuenm2_0.1.3.tar.gz(r-4.7-any)kuenm2_0.1.3.tar.gz(r-4.6-any)
kuenm2_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
kuenm2/json (API)
NEWS

# Install 'kuenm2' in R:
install.packages('kuenm2', repos = c('https://marlonecobos.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/marlonecobos/kuenm2/issues

Pkgdown/docs site:https://marlonecobos.github.io

Datasets:

On CRAN:

Conda:

8.05 score 12 stars 25 scripts 532 downloads 48 exports 24 dependencies

Last updated from:63fbf0cf7b. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK268
source / vignettesOK391
linux-release-x86_64OK252
macos-release-arm64OK211
macos-oldrel-arm64OK227
windows-develOK186
windows-releaseOK207
windows-oldrelOK228
wasm-releaseOK146

Exports:advanced_cleaningall_response_curvesbinarize_changesbivariate_responsecalibrationcolors_for_changesdetect_concaveexplore_calibration_histexplore_partition_envexplore_partition_extrapolationexplore_partition_geoextract_occurrence_variablesextract_var_from_formulasfilter_decimal_precisionfit_selectedglm_mxglmnet_mximport_resultsindependent_evaluationinitial_cleaningmove_2closest_cellorganize_for_projectionorganize_future_worldclimpartial_rocpartition_response_curvesperform_pcaplot_calibration_histplot_explore_partitionplot_importancepredict_selectedpredict.glmnet_mxprediction_changesprepare_dataprepare_projectionprepare_user_dataproject_selectedprojection_changesprojection_mopprojection_variabilityremove_cell_duplicatesremove_corrdinates_00remove_duplicatesremove_missingresponse_curveselect_modelssingle_mopsort_columnsvariable_importance

Dependencies:clustercodetoolsdoSNOWellipseenmpaforeachfpROCglmnetiteratorslatticeMASSMatrixmgcvmopnlmepermuteRcppRcppArmadilloRcppEigenshapesnowsurvivalterravegan

Basic Data Cleaning

Rendered frombasic_data_cleaning.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2026-04-20
Started: 2025-07-19

Prepare Data for Model Calibration

Rendered fromprepare_data.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2026-04-20
Started: 2025-06-05

Model Calibration

Rendered frommodel_calibration.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2026-04-20
Started: 2025-06-20

Fit and Explore Selected Models

Rendered frommodel_exploration.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2026-04-20
Started: 2025-06-25

Project Models to a Single Scenario

Rendered frommodel_predictions.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2026-04-20
Started: 2025-06-27

Project Models to Multiple Scenarios

Rendered frommodel_projections.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2026-04-20
Started: 2025-07-07

Exploring Model Uncertainty and Variability

Rendered fromvariability_and_uncertainty.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2026-04-20
Started: 2025-07-19

Example of Projections Using CHELSA Data

Rendered fromprojections_chelsa.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2026-04-20
Started: 2025-08-02

Readme and manuals

Help Manual

Help pageTopics
kuenm2: Detailed Development of Ecological Niche Modelskuenm2-package kuenm2
Advanced occurrence data cleaningadvanced_cleaning move_2closest_cell remove_cell_duplicates
Example Bias Filebias
Binarize changes based on the agreement among GCMsbinarize_changes
Bivariate response plot for fitted modelsbivariate_response
Calibration Results (glm)calib_results_glm
Calibration Results (Maxnet)calib_results_maxnet
Fitting and evaluation of models, and selection of the best onescalibration
SpatRaster Representing present-day Conditions (CHELSA)chelsa_current
SpatRaster Representing LGM Conditions (GCM: CCSM4)chelsa_lgm_ccsm4
SpatRaster Representing LGM Conditions (GCM: CNRM-CM5)chelsa_lgm_cnrm_cm5
SpatRaster Representing LGM Conditions (GCM: FGOALS-g2)chelsa_lgm_fgoals_g2
SpatRaster Representing LGM Conditions (GCM: IPSL-CM5A-LR)chelsa_lgm_ipsl
SpatRaster Representing LGM Conditions (GCM: MIROC-ESM)chelsa_lgm_miroc
SpatRaster Representing LGM Conditions (GCM: MPI-ESM-P)chelsa_lgm_mpi
SpatRaster Representing LGM Conditions (GCM: MRI-CGCM3)chelsa_lgm_mri
Set Colors for Change Mapscolors_for_changes
Detect concave curves in GLM and GLMNET modelsdetect_concave
Spatial Blocks from ENMevalenmeval_block
Explore variable distribution for occurrence and background pointsexplore_calibration_hist
Explore the Distribution of Partitions in Environmental Spaceexplore_partition_env
Analysis of extrapolation risks in partitions using the MOP metricexplore_partition_extrapolation
Explore the spatial distribution of partitions for occurrence and background pointsexplore_partition_geo
Extracts Environmental Variables for Occurrencesextract_occurrence_variables
Extract predictor names from formulasextract_var_from_formulas
Fit models selected after calibrationfit_selected
Fitted model with CHELSA variablesfitted_model_chelsa
Fitted model with concave curvesfitted_model_concave
Fitted model with glm algorithmfitted_model_glm
Fitted model with maxnet algorithmfitted_model_maxnet
Spatial Blocks from flexsdmflexsdm_block
SpatRaster Representing Future Conditions (2041-2060, SSP126, GCM: ACCESS-CM2)future_2050_ssp126_access
SpatRaster Representing Future Conditions (2041-2060, SSP126, GCM: MIROC6)future_2050_ssp126_miroc
SpatRaster Representing Future Conditions (2041-2060, SSP585, GCM: ACCESS-CM2)future_2050_ssp585_access
SpatRaster Representing Future Conditions (2041-2060, SSP585, GCM: MIROC6)future_2050_ssp585_miroc
SpatRaster Representing Future Conditions (2081-2100, SSP126, GCM: ACCESS-CM2)future_2100_ssp126_access
SpatRaster Representing Future Conditions (2081-2100, SSP126, GCM: MIROC6)future_2100_ssp126_miroc
SpatRaster Representing Future Conditions (2081-2100, SSP585, GCM: ACCESS-CM2)future_2100_ssp585_access
SpatRaster Representing Future Conditions (2081-2100, SSP585, GCM: MIROC6)future_2100_ssp585_miroc
Maxent-like Generalized Linear Models (GLM)glm_mx
Maxent-like glmnet modelsglmnet_mx
Import rasters resulting from projection functionsimport_results
Evaluate models with independent dataindependent_evaluation
Initial occurrence data cleaning stepsfilter_decimal_precision initial_cleaning remove_corrdinates_00 remove_duplicates remove_missing sort_columns
Discrete palettes based on pals R packagekuenm2_discrete_palletes
SpatVector Representing Calibration Area for _Myrcia hatschbachii_m
Independent Species Occurrencenew_occ
Species Occurrenceocc_data
Species Occurrence with Erroneous Recordsocc_data_noclean
Organize and structure variables for past and future projectionsorganize_for_projection
Organize and structure future climate variables from WorldClimorganize_future_worldclim
Partial ROC calculation for multiple candidate modelspartial_roc
Response curves for selected models according to training/testing partitionspartition_response_curves
Principal Component Analysis for raster layersperform_pca
Histograms to visualize data from explore_calibration objectsplot_calibration_hist
Plot extrapolation risks for partitionsplot_explore_partition
Summary plot for variable importance in modelsplot_importance
Predict method for glmnet_mx (maxnet) modelspredict predict,kuenm2_glmnet_mx-method predict.glmnet_mx
Predict selected models for a single scenariopredict_selected
Compute changes of suitable areas in other scenarios (single scenario / GCM)prediction_changes
Prepare data for model calibrationprepare_data
Preparation of data for model projectionsprepare_projection
Prepare data for model calibration with user-prepared calibration dataprepare_user_data
Print method for kuenm2 objectsprint print,kuenm2_calibration_results-method print,kuenm2_fitted_models-method print,kuenm2_model_projections-method print,kuenm2_prepared_data-method print,kuenm2_projection_data-method print.calibration_results print.fitted_models print.model_projections print.prepared_data print.projection_data
Project selected models to multiple sets of new data (scenarios)project_selected
Compute changes of suitable areas between scenariosprojection_changes
Analysis of extrapolation risks in projections using the MOP metricprojection_mop
Explores variance coming from distinct sources in model predictionsprojection_variability
Variable response curves for fitted modelsall_response_curves response_curve
Select models that perform the best among candidatesselect_models
Analysis of extrapolation risks using the MOP metric (for single scenario)single_mop
Prepared Data for maxnet modelssp_swd
Prepared Data for glm modelssp_swd_glm
Prepared data with spatial blocks created with ENMevalswd_spatial_block
User Custom Calibration Datauser_data
SpatRaster Representing present-day Conditions (WorldClim)var
Variable importancevariable_importance
World country polygons from Natural Earthworld