calc_performance_metrics
                        Calculates performance metrics of a
                        business-as-usual model
calc_summary_statistics
                        Calculates summary statistics for predictions
                        and true values
clean_data              Clean and Optionally Aggregate Environmental
                        Data
copy_default_params     Copy Default Parameters File
detrend                 Removes trend from data
estimate_effect_size    Estimates size of the external effect
get_meteo_available     Get Available Meteorological Components
load_params             Load Parameters from YAML File
load_uba_data_from_dir
                        Load UBA Data from Directory
mock_env_data           Mock Environmental Data
plot_counterfactual     Prepare Plot Data and Plot Counterfactuals
plot_station_measurements
                        Descriptive plot of daily time series data
prepare_data_for_modelling
                        Prepare Data for Training a model
rescale_predictions     Rescale predictions to original scale.
retrend_predictions     Restors the trend in the prediction
run_counterfactual      Full counterfactual simulation run
run_dynamic_regression
                        Run the dynamic regression model
run_fnn                 Train a Feedforward Neural Network (FNN) in a
                        Counterfactual Scenario.
run_lightgbm            Run gradient boosting model with lightgbm
run_rf                  Run random forest model with ranger
sample_data_DESN025     Environmental Data for Modelling from station
                        DESN025 in Leipzig-Mitte.
scale_data              Standardize Training and Application Data
split_data_counterfactual
                        Split Data into Training and Application
                        Datasets
