Package: kuenm2 0.1.3
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:
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
- calib_results_glm - Calibration Results
- calib_results_maxnet - Calibration Results
- enmeval_block - Spatial Blocks from ENMeval
- fitted_model_chelsa - Fitted model with CHELSA variables
- fitted_model_concave - Fitted model with concave curves
- fitted_model_glm - Fitted model with glm algorithm
- fitted_model_maxnet - Fitted model with maxnet algorithm
- flexsdm_block - Spatial Blocks from flexsdm
- kuenm2_discrete_palletes - Discrete palettes based on pals R package
- new_occ - Independent Species Occurrence
- occ_data - Species Occurrence
- occ_data_noclean - Species Occurrence with Erroneous Records
- sp_swd - Prepared Data for maxnet models
- sp_swd_glm - Prepared Data for glm models
- swd_spatial_block - Prepared data with spatial blocks created with ENMeval
- user_data - User Custom Calibration Data
Last updated from:63fbf0cf7b. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 268 | ||
| source / vignettes | OK | 391 | ||
| linux-release-x86_64 | OK | 252 | ||
| macos-release-arm64 | OK | 211 | ||
| macos-oldrel-arm64 | OK | 227 | ||
| windows-devel | OK | 186 | ||
| windows-release | OK | 207 | ||
| windows-oldrel | OK | 228 | ||
| wasm-release | OK | 146 |
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
