Package index • kuenm2

All functions

advanced_cleaning() remove_cell_duplicates() move_2closest_cell(): Advanced occurrence data cleaning

bias: Example Bias File

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_geo(): Explore the spatial distribution of occurrence and background points

explore_calibration_hist(): Explore variable distribution for occurrence and background points

explore_partition_extrapolation(): Analysis of extrapolation risks in partitions using the MOP metric

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

enmeval_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_projections(): Import rasters resulting from projection functions

independent_evaluation(): Evaluate models with independent data

initial_cleaning() sort_columns() remove_missing() remove_duplicates() remove_corrdinates_00() filter_decimal_precision(): Initial occurrence data cleaning steps

kuenm2 kuenm2-package: kuenm2: Detailed Development of Ecological Niche Models

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

perform_pca(): Principal Component Analysis for raster layers

plot_explore_calibration(): Histograms to visualize data from explore_calibration objects

plot_importance(): Summary plot for variable importance in models

predict.glmnet_mx(): Predict method for glmnet_mx (maxnet) models

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(<prepared_data>) print(<calibration_results>) print(<fitted_models>) print(<projection_data>) print(<model_projections>): 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() all_response_curves(): Variable response curves for fitted models

select_models(): Select models that perform the best among candidates

sp_swd: Prepared Data for maxnet models

sp_swd_glm: Prepared Data for glm models

user_data: User Custom Calibration Data

var: SpatRaster Representing present-day Conditions (WorldClim)

variable_importance(): Variable importance