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
calib_results_glm       Calibration Results (glm)
calib_results_maxnet    Calibration Results (Maxnet)
calibration             Fitting and evaluation of models, and selection
                        of the best ones
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
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, 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)
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-package          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_
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_selected        Predict selected models for a single scenario
prediction_changes      Compute changes of suitable areas in other
                        scenarios (single scenario / GCM)
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
project_selected        Project selected models to multiple sets of new
                        data (scenarios)
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
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)
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
