Summary | Description | Getting ready | Preparing data | Example data | First steps in preparing data | Using pre-processed data | Exploring prepared data | Comparative histograms | Distribution of data for models | Similarity assessment in partitions | More options to prepare data | Custom formulas | Using a bias file | PCA for variables | Internal PCA | External PCA | Using external data partitions | ENMeval partitions | flexsdm partitions | Saving prepared data
Package: kuenm2 0.1.4
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:
kuenm2_0.1.4.tar.gz
kuenm2_0.1.4.zip(r-4.7-any)kuenm2_0.1.4.zip(r-4.6-any)kuenm2_0.1.4.zip(r-4.5-any)
kuenm2_0.1.4.tgz(r-4.6-any)kuenm2_0.1.4.tgz(r-4.5-any)
kuenm2_0.1.4.tar.gz(r-4.7-any)kuenm2_0.1.4.tar.gz(r-4.6-any)
kuenm2_0.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
kuenm2/json (API)
| # 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:
- 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:470abb721e. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 255 | ||
| source / vignettes | OK | 321 | ||
| linux-release | OK | 221 | ||
| macos-release | OK | 210 | ||
| macos-oldrel | OK | 177 | ||
| windows-devel | OK | 239 | ||
| windows-release | OK | 222 | ||
| windows-oldrel | OK | 288 | ||
| wasm-release | OK | 157 |
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_missingreplace_partitionresponse_curveselect_modelssingle_mopsort_columnsvariable_importance
Dependencies:clustercodetoolsdoSNOWellipseenmpaforeachfpROCglmnetiteratorslatticeMASSMatrixmgcvmopnlmepermuteRcppRcppArmadilloRcppEigenshapesnowsurvivalterravegan
Prepare Data for Model Calibration
Last update: 2026-10-01
Started: 2025-06-05
Basic Data Cleaning
Summary | Description | Getting ready | Cleaning data | Import data | Basic cleaning steps | Other cleaning steps | Saving results
Last update: 2026-04-20
Started: 2025-07-19
Model Calibration
Summary | Description | Getting ready | Preparing data | Calibration | Maxnet models | GLMs | Concave curves | Re-selecting models | Training partition effects | Saving a calibration_results object
Last update: 2026-04-20
Started: 2025-06-20
Fit and Explore Selected Models
Summary | Description | Getting ready | Fitting selected models | Response curves | All response curves | Customized response curves | Bivariate response curves | Variable importance | Evaluation with independent data | Saving a fitted_models object
Last update: 2026-04-20
Started: 2025-06-25
Project Models to a Single Scenario
Summary | Description | Getting ready | Fitted models | Model predictions | Predict to SpatRaster | Predict to data.frame | Options for predictions | Output type | Clamping variables | No extrapolation | Binarize predictions | Saving predictions | Detecting changes in predictions
Last update: 2026-04-20
Started: 2025-06-27
Project Models to Multiple Scenarios
Summary | Description | Getting ready | Fitted models | Variables for projections | Variables from WorldClim | Format for renaming | Static variables | Organizing files | Preparing data for projections | Projecting to multiple scenarios | Importing results from projections | Detecting changes in projections | Setting colors for maps | Types os results | Importing Results | Saving results
Last update: 2026-04-20
Started: 2025-07-07
Exploring Model Uncertainty and Variability
Summary | Description | Getting ready | Loading and preparing data | Exploring variability | Importing Results | Saving results | Assessing extrapolation risks | MOP result options | Distances | Basic | Simple | Towards high and low ends | Combined results | Handling values within range | MOP results and response curves | Saving and importing results
Last update: 2026-04-20
Started: 2025-07-19
Example of Projections Using CHELSA Data
Summary | Description | Getting ready | Naming files for projections | Getting variables | Downloading LGM variables | Downloading current variables | Subsetting and renaming | Organizing variables | Data preparation for model projections | Projecting models | Detecting changes: Past vs present
Last update: 2026-04-20
Started: 2025-08-02
