add_baseline | Internal Function: Add baselines after second-step logistic regression (part of AutoScore Module 3) |

assign_score | Internal Function: Automatically assign scores to each subjects given new data set and scoring table (Used for intermediate and final evaluation) |

AutoScore_fine_tuning | AutoScore STEP(iv): Fine-tune the score by revising cut_vec with domain knowledge (AutoScore Module 5) |

AutoScore_fine_tuning_Ordinal | AutoScore STEP(iv) for ordinal outcomes: Fine-tune the score by revising 'cut_vec' with domain knowledge (AutoScore Module 5) |

AutoScore_fine_tuning_Survival | AutoScore STEP(iv) for survival outcomes: Fine-tune the score by revising cut_vec with domain knowledge (AutoScore Module 5) |

AutoScore_parsimony | AutoScore STEP(ii): Select the best model with parsimony plot (AutoScore Modules 2+3+4) |

AutoScore_parsimony_Ordinal | AutoScore STEP(ii) for ordinal outcomes: Select the best model with parsimony plot (AutoScore Modules 2+3+4) |

AutoScore_parsimony_Survival | AutoScore STEP(ii) for survival outcomes: Select the best model with parsimony plot (AutoScore Modules 2+3+4) |

AutoScore_rank | AutoScore STEP(i): Rank variables with machine learning (AutoScore Module 1) |

AutoScore_rank_Ordinal | AutoScore STEP (i) for ordinal outcomes: Generate variable ranking list by machine learning (AutoScore Module 1) |

AutoScore_rank_Survival | AutoScore STEP (1) for survival outcomes: Generate variable ranking List by machine learning (Random Survival Forest) (AutoScore Module 1) |

AutoScore_testing | AutoScore STEP(v): Evaluate the final score with ROC analysis (AutoScore Module 6) |

AutoScore_testing_Ordinal | AutoScore STEP(v) for ordinal outcomes: Evaluate the final score (AutoScore Module 6) |

AutoScore_testing_Survival | AutoScore STEP(v) for survival outcomes: Evaluate the final score with ROC analysis (AutoScore Module 6) |

AutoScore_weighting | AutoScore STEP(iii): Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3) |

AutoScore_weighting_Ordinal | AutoScore STEP(iii) for ordinal outcomes: Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3) |

AutoScore_weighting_Survival | AutoScore STEP(iii) for survival outcomes: Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3) |

change_reference | Internal Function: Change Reference category after first-step logistic regression (part of AutoScore Module 3) |

check_data | AutoScore function for datasets with binary outcomes: Check whether the input dataset fulfill the requirement of the AutoScore |

check_data_ordinal | AutoScore function for ordinal outcomes: Check whether the input dataset fulfil the requirement of the AutoScore |

check_data_survival | AutoScore function for survival data: Check whether the input dataset fulfill the requirement of the AutoScore |

check_link | Internal function: Check link function |

check_predictor | Internal function: Check predictors |

compute_auc_val | Internal function: Compute AUC based on validation set for plotting parsimony (AutoScore Module 4) |

compute_auc_val_ord | Internal function: Compute mean AUC for ordinal outcomes based on validation set for plotting parsimony |

compute_auc_val_survival | Internal function for survival outcomes: Compute AUC based on validation set for plotting parsimony |

compute_descriptive_table | AutoScore function: Descriptive Analysis |

compute_final_score_ord | Internal function: Compute risk scores for ordinal data given variables selected, cut-off values and scoring table |

compute_mauc_ord | Internal function: Compute mAUC for ordinal predictions |

compute_multi_variable_table | AutoScore function: Multivariate Analysis |

compute_multi_variable_table_ordinal | AutoScore-Ordinal function: Multivariate Analysis |

compute_multi_variable_table_survival | AutoScore function for survival outcomes: Multivariate Analysis |

compute_prob_observed | Internal function: Based on given labels and scores, compute proportion of subjects observed in each outcome category in given score intervals. |

compute_prob_predicted | Internal function: Based on given labels and scores, compute average predicted risks in given score intervals. |

compute_score_table | Internal function: Compute scoring table based on training dataset (AutoScore Module 3) |

compute_score_table_ord | Internal function: Compute scoring table for ordinal outcomes based on training dataset |

compute_score_table_survival | Internal function: Compute scoring table for survival outcomes based on training dataset |

compute_uni_variable_table | AutoScore function: Univariable Analysis |

compute_uni_variable_table_ordinal | AutoScore-Ordinal function: Univariable Analysis |

compute_uni_variable_table_survival | AutoScore function for survival outcomes: Univariate Analysis |

conversion_table | AutoScore function: Print conversion table based on final performance evaluation |

conversion_table_ordinal | AutoScore function: Print conversion table for ordinal outcomes to map score to risk |

conversion_table_survival | AutoScore function for survival outcomes: Print conversion table |

estimate_p_mat | Internal function: generate probability matrix for ordinal outcomes given thresholds, linear predictor and link function |

evaluate_model_ord | Internal function: Evaluate model performance on ordinal data |

eva_performance_iauc | Internal function survival outcome: Calculate iAUC for validation set |

extract_or_ci_ord | Extract OR, CI and p-value from a proportional odds model |

find_one_inds | Internal function: Find column indices in design matrix that should be 1 |

find_possible_scores | Internal function: Compute all scores attainable. |

get_cut_vec | Internal function: Calculate cut_vec from the training set (AutoScore Module 2) |

group_score | Internal function: Group scores based on given score breaks, and use friendly names for first and last intervals. |

induce_informative_missing | Internal function: induce informative missing to sample data in the package to demonstrate how AutoScore handles missing as a separate category |

induce_median_missing | Internal function: induce informative missing in a single variable |

inv_cloglog | Internal function: Inverse cloglog link |

inv_logit | Internal function: Inverse logit link |

inv_probit | Internal function: Inverse probit link |

make_design_mat | Internal function: Based on 'find_one_inds', make a design matrix to compute all scores attainable. |

plot_auc | Internal function: Make parsimony plot |

plot_importance | Internal Function: Print plotted variable importance |

plot_predicted_risk | AutoScore function for binary and ordinal outcomes: Plot predicted risk |

plot_roc_curve | Internal Function: Plotting ROC curve |

plot_survival_km | AutoScore function for survival outcomes: Print scoring performance (KM curve) |

print_performance_ci_survival | AutoScore function for survival outcomes: Print predictive performance with confidence intervals |

print_performance_ordinal | AutoScore function for ordinal outcomes: Print predictive performance |

print_performance_survival | AutoScore function for survival outcomes: Print predictive performance |

print_roc_performance | AutoScore function: Print receiver operating characteristic (ROC) performance |

print_scoring_table | AutoScore Function: Print scoring tables for visualization |

sample_data | 20000 simulated ICU admission data, with the same distribution as the data in the MIMIC-III ICU database |

sample_data_ordinal | Simulated ED data with ordinal outcome |

sample_data_ordinal_small | Simulated ED data with ordinal outcome (small sample size) |

sample_data_small | 1000 simulated ICU admission data, with the same distribution as the data in the MIMIC-III ICU database |

sample_data_survival | 20000 simulated MIMIC sample data with survival outcomes |

sample_data_survival_small | 1000 simulated MIMIC sample data with survival outcomes |

sample_data_with_missing | 20000 simulated ICU admission data with missing values |

split_data | AutoScore Function: Automatically splitting dataset to train, validation and test set, possibly stratified by label |

transform_df_fixed | Internal function: Categorizing continuous variables based on cut_vec (AutoScore Module 2) |