| kuenm2-package |
kuenm2: Detailed Development of Ecological Niche Models |
| advanced_cleaning |
Advanced occurrence data cleaning |
| all_response_curves |
Variable response curves for fitted models |
| 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 ones |
| 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 points |
| explore_partition_env |
Explore the Distribution of Partitions in Environmental Space |
| explore_partition_extrapolation |
Analysis of extrapolation risks in partitions using the MOP metric |
| explore_partition_geo |
Explore the spatial distribution of partitions for occurrence and background points |
| extract_occurrence_variables |
Extracts Environmental Variables for Occurrences |
| extract_var_from_formulas |
Extract predictor names from formulas |
| filter_decimal_precision |
Initial occurrence data cleaning steps |
| 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 |
| fit_selected |
Fit models selected after calibration |
| flexsdm_block |
Spatial Blocks from flexsdm |
| future_2050_ssp126_access |
SpatRaster Representing Future Conditions (2041-2060, SSP126, GCM: ACCESS-CM2) |
| future_2050_ssp126_miroc |
SpatRaster Representing Future Conditions (2041-2060, SSP126, GCM: MIROC6) |
| future_2050_ssp585_access |
SpatRaster Representing Future Conditions (2041-2060, SSP585, GCM: ACCESS-CM2) |
| future_2050_ssp585_miroc |
SpatRaster Representing Future Conditions (2041-2060, SSP585, GCM: MIROC6) |
| future_2100_ssp126_access |
SpatRaster Representing Future Conditions (2081-2100, SSP126, GCM: ACCESS-CM2) |
| future_2100_ssp126_miroc |
SpatRaster Representing Future Conditions (2081-2100, SSP126, GCM: MIROC6) |
| future_2100_ssp585_access |
SpatRaster Representing Future Conditions (2081-2100, SSP585, GCM: ACCESS-CM2) |
| future_2100_ssp585_miroc |
SpatRaster Representing Future Conditions (2081-2100, SSP585, GCM: MIROC6) |
| glmnet_mx |
Maxent-like glmnet models |
| glm_mx |
Maxent-like Generalized Linear Models (GLM) |
| import_results |
Import rasters resulting from projection functions |
| independent_evaluation |
Evaluate models with independent data |
| initial_cleaning |
Initial occurrence data cleaning steps |
| kuenm2 |
kuenm2: Detailed Development of Ecological Niche Models |
| kuenm2_discrete_palletes |
Discrete palettes based on pals R package |
| m |
SpatVector Representing Calibration Area for _Myrcia hatschbachii_ |
| move_2closest_cell |
Advanced occurrence data cleaning |
| 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 projections |
| organize_future_worldclim |
Organize and structure future climate variables from WorldClim |
| partial_roc |
Partial ROC calculation for multiple candidate models |
| partition_response_curves |
Response curves for selected models according to training/testing partitions |
| 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 |
| predict-method |
Predict method for glmnet_mx (maxnet) models |
| predict.glmnet_mx |
Predict method for glmnet_mx (maxnet) models |
| prediction_changes |
Compute changes of suitable areas in other scenarios (single scenario / GCM) |
| 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 calibration data |
| print |
Print method for kuenm2 objects |
| print-method |
Print method for kuenm2 objects |
| print.calibration_results |
Print method for kuenm2 objects |
| print.fitted_models |
Print method for kuenm2 objects |
| print.model_projections |
Print method for kuenm2 objects |
| print.prepared_data |
Print method for kuenm2 objects |
| print.projection_data |
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 metric |
| projection_variability |
Explores variance coming from distinct sources in model predictions |
| project_selected |
Project selected models to multiple sets of new data (scenarios) |
| remove_cell_duplicates |
Advanced occurrence data cleaning |
| remove_corrdinates_00 |
Initial occurrence data cleaning steps |
| remove_duplicates |
Initial occurrence data cleaning steps |
| remove_missing |
Initial occurrence data cleaning steps |
| 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 single scenario) |
| sort_columns |
Initial occurrence data cleaning steps |
| 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 |
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