Package index • spatialcva

All functions

apply_sensitivity_logic_rule(): Calculate Total Sensitivity Score with the Logic Rule
avg_model_data(): Average Model Data
bhatt_coeff_brt(): Bhattacharyya Coefficient of two distributions
bhattacharyya_stat_brt(): Bhattacharyya's coefficient test
build_fisheries_raster(): Convert Standardized Fisheries Data into Presence/Absence/Effort Raster
build_sdm(): Build Component or Ensemble Species Distribution Models
calculate_attribute_score(): Calculate Attribute Scores
calculate_data_quality(): Calculate Data Quality Scores for each Attribute & Species
calculate_directionality(): Calculate Directionality Score
calculate_directionality_certainty(): Calculate Certainty on Bootstrapped Directionality Scores
calculate_distribution_shifts(): Calculate Change in Distribution Metrics
calculate_model_confidence(): Calculate Model Confidence Score for each Species
calculate_raw_exposure(): Calculate Raw Exposure from Environmental Data
calculate_sdm_variable_importance(): Calculate Variable Importance
calculate_sensitivity(): Calculate Sensitivity
calculate_sensitivity_certainty(): Calculate Certainty on Bootstrapped Sensitivity Scores
cross_validate_sdm(): Component Model Cross-Validation
eval_brt(): Calculate BRT model evaluation statistics
eval_kfold_brt(): K-fold BRT fit
evaluate_ensemble(): Test the Ensemble Model with Observations
evaluate_sdm(): Calculate Performance Metric
make_evaluation_csv(): Create a spreadsheet containing all SDM performance metrics
make_exposure_plots(): Make Plots of Exposure Results
make_exposure_table(): Make Exposure Summary Table
make_sdm_plots(): Make Plots for Species Distribution Models
make_sdm_predictions(): Predict Component or Ensemble SDM
make_sdm_reports(): Render Species Distribution Model (SDM) reports
make_sensitivity_barplots(): Make Sensitivity Barplot Reports
make_sensitivity_table(): Make Sensitivity Table
make_total_exposure(): Make Map or Timeseries of Total Species-Specific Exposure
make_variable_exposure(): Make Maps or Timeseries of Variable-Specific Exposure
match_guilds(): Subset Species/Environmental Dataset to Ecologically-Relevant Dynamic Variables
match_pa_model_rasters(): Match Species and Model Rasters and Create a Data Frame
merge_fisheries_rasters(): Merge Fisheries Presence/Absence/Effort Rasters
normalize_model_data(): Normalize Model Data
normalize_variable_weights(): Normalize Dynamic Variable Weights
predict_to_raster(): Convert Predicted Values in Data.Frame to Rasters
pseudo_r2_brt(): calculate Pseudo-R2 for BRT
pull_mom6_forecast(): Pull MOM6 Forecast Data
pull_mom6_hindcast(): Pull MOM6 Hindcast Data
pull_sdm_preds(): Pull Predicted Values from SDMs CV
rank_exposure(): Rank Raw Environmental Exposure
raster_to_df(): Convert Environmental Rasters to a Data.Frame
remove_corr(): Remove Correlated Environmental Covariates from Species/Environmental Data Frame
save_auc_brt(): Calculate Area under the Curve (AUC) for BRT models
save_tss_brt(): Calculate True Skill Statistic (TSS) for BRT models
sd_model_data(): Calculate Standard Deviation on MOM6 Data
set_binary_pa(): Convert Species/Environmental Data Frame to Presence/Absence
standardize_fisheries_data(): Standardize Fisheries Dependent and Independent Datasets