Package index • kuenm2

All functions

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

bivariate_response(): Bivariate response plot for fitted models

calibration(): Fitting and evaluation of models, and selection of the best ones

detect_concave(): Detect concave curves in GLM and GLMNET models

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

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

extract_var_from_formulas(): Extract predictor names from formulas

fit_selected(): Fit models selected after calibration

glm_mx(): Maxent-like Generalized Linear Models (GLM)

glmnet_mx(): Maxent-like glmnet models

import_projections(): Import rasters resulting from projection functions

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

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(): Variable response curves for fitted models

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

variable_importance(): Variable importance