| advanced_cleaning | Advanced occurrence data cleaning |
| bias | Example Bias File |
| binarize_changes | Binarize changes based on the agreement among GCMs |
| bivariate_response | Bivariate response plot for fitted models |
| calibration | Fitting and evaluation of models, and selection of the best... |
| calib_results_glm | Calibration Results (glm) |
| calib_results_maxnet | Calibration Results (Maxnet) |
| chelsa_current | SpatRaster Representing present-day Conditions (CHELSA) |
| chelsa_lgm_ccsm4 | SpatRaster Representing LGM Conditions (GCM: CCSM4) |
| chelsa_lgm_cnrm_cm5 | SpatRaster Representing LGM Conditions (GCM: CNRM-CM5) |
| chelsa_lgm_fgoals_g2 | SpatRaster Representing LGM Conditions (GCM: FGOALS-g2) |
| chelsa_lgm_ipsl | SpatRaster Representing LGM Conditions (GCM: IPSL-CM5A-LR) |
| chelsa_lgm_miroc | SpatRaster Representing LGM Conditions (GCM: MIROC-ESM) |
| chelsa_lgm_mpi | SpatRaster Representing LGM Conditions (GCM: MPI-ESM-P) |
| chelsa_lgm_mri | SpatRaster Representing LGM Conditions (GCM: MRI-CGCM3) |
| colors_for_changes | Set Colors for Change Maps |
| detect_concave | Detect concave curves in GLM and GLMNET models |
| enmeval_block | Spatial Blocks from ENMeval |
| explore_calibration_hist | Explore variable distribution for occurrence and background... |
| explore_partition_env | Explore the Distribution of Partitions in Environmental Space |
| explore_partition_extrapolation | Analysis of extrapolation risks in partitions using the MOP... |
| explore_partition_geo | Explore the spatial distribution of partitions for occurrence... |
| extract_occurrence_variables | Extracts Environmental Variables for Occurrences |
| extract_var_from_formulas | Extract predictor names from formulas |
| fit_selected | Fit models selected after calibration |
| 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 |
| future_2050_ssp126_access | SpatRaster Representing Future Conditions (2041-2060, SSP126,... |
| future_2050_ssp126_miroc | SpatRaster Representing Future Conditions (2041-2060, SSP126,... |
| future_2050_ssp585_access | SpatRaster Representing Future Conditions (2041-2060, SSP585,... |
| future_2050_ssp585_miroc | SpatRaster Representing Future Conditions (2041-2060, SSP585,... |
| future_2100_ssp126_access | SpatRaster Representing Future Conditions (2081-2100, SSP126,... |
| future_2100_ssp126_miroc | SpatRaster Representing Future Conditions (2081-2100, SSP126,... |
| future_2100_ssp585_access | SpatRaster Representing Future Conditions (2081-2100, SSP585,... |
| future_2100_ssp585_miroc | SpatRaster Representing Future Conditions (2081-2100, SSP585,... |
| glm_mx | Maxent-like Generalized Linear Models (GLM) |
| glmnet_mx | Maxent-like glmnet models |
| import_results | Import rasters resulting from projection functions |
| independent_evaluation | Evaluate models with independent data |
| initial_cleaning | Initial occurrence data cleaning steps |
| kuenm2_discrete_palletes | Discrete palettes based on pals R package |
| kuenm2-package | kuenm2: Detailed Development of Ecological Niche Models |
| m | SpatVector Representing Calibration Area for _Myrcia... |
| new_occ | Independent Species Occurrence |
| occ_data | Species Occurrence |
| occ_data_noclean | Species Occurrence with Erroneous Records |
| organize_for_projection | Organize and structure variables for past and future... |
| organize_future_worldclim | Organize and structure future climate variables from... |
| partial_roc | Partial ROC calculation for multiple candidate models |
| partition_response_curves | Response curves for selected models according to... |
| perform_pca | Principal Component Analysis for raster layers |
| plot_calibration_hist | Histograms to visualize data from explore_calibration objects |
| plot_explore_partition | Plot extrapolation risks for partitions |
| plot_importance | Summary plot for variable importance in models |
| predict | Predict method for glmnet_mx (maxnet) models |
| prediction_changes | Compute changes of suitable areas in other scenarios (single... |
| predict_selected | Predict selected models for a single scenario |
| prepare_data | Prepare data for model calibration |
| prepare_projection | Preparation of data for model projections |
| prepare_user_data | Prepare data for model calibration with user-prepared... |
| print | Print method for kuenm2 objects |
| projection_changes | Compute changes of suitable areas between scenarios |
| projection_mop | Analysis of extrapolation risks in projections using the MOP... |
| projection_variability | Explores variance coming from distinct sources in model... |
| project_selected | Project selected models to multiple sets of new data... |
| response_curve | Variable response curves for fitted models |
| select_models | Select models that perform the best among candidates |
| single_mop | Analysis of extrapolation risks using the MOP metric (for... |
| 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 |
| var | SpatRaster Representing present-day Conditions (WorldClim) |
| variable_importance | Variable importance |
| world | World country polygons from Natural Earth |
