Package index

Data preparation

prepare_powerup_data()

Prepare data for POWERUP model training and prediction

Model training and evaluation

fit_powerup_models()

Fit POWERUP models

summarize_models()

Summarize models

Prediction

add_powerup_predictions()

Add POWERUP predictions to model objects

summarize_predictions()

Summarize predictions

summarize_error()

Summarize prediction error

get_variable_predictions()

Find most variable predictions

Explanation

calculate_explanation_paths()

Calculate training explanation paths

assign_explanation_paths()

Assign prediction samples to explanation paths

summarize_explanation_paths()

Summarize explanation paths

get_explanation_path_assignments()

Get explanation-path assignments

plot_explanation_paths()

Plot explanation paths for one target

summarize_contributions()

Summarize SHAP values

plot_top_contributors()

Plot top contributions

plot_contributions_to_sample()

Plot feature contributions to selected samples

plot_shap_scatter()

Plot SHAP values vs feature values

Experimental observations

prepare_powerup_observations()

Prepare experimental observations for POWERUP

parse_powerup_barcode_sample_names()

Parse POWERUP barcode-count sample names

Posterior updating

calculate_powerup_posteriors()

Calculate POWERUP posterior response distributions

plot_posterior()

Plot a POWERUP posterior update

Low-level modeling

Lower-level functions used by the higher-level POWERUP workflow.

make_xgb_model()

Fit one POWERUP model

make_new_data_predictions()

Use a model to make predictions on new data