# Detailed Development of Ecological Niche Models

## Documentation for package ‘kuenm2’ version 0.1.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

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