Package index • demcrit

All functions

assign_cognitive_impairment(): Assign Cognitive Impairment Status
check_compatibility(): Check Data Compatibility
check_names(): Check Patient Names
check_ranges(): Check Value Ranges of Relevant Variables
compute_descriptives(): Describe the Sample
compute_predictions(): Compute model predictions
compute_z_score(): Compute Regression z-scores
concords: Table of Pairwise Concordance Measures
correlate_indexes(): Compute correlation between test indexes
data_paths(): Define Raw Data Paths
derive_scoring_rule(): Derive a scoring rule for PDD
describe_concordance(): Compute Concordance Measures
diagnose_pdd_case(): Diagnose via a Single Algorithm
diagnose_pdd_sample(): Diagnose All Patients
do_summary(): Summarise Data
extract_coefficients(): Extract point estimate of model coefficients
find_incomplete(): Find patients with missing data
fit_reference(): Fit Reference Model for Projective Prediction
grid_models(): Fit a grid of reference models
grid_postpred(): Grid posterior predictions of a model
gt_apa_table(): Generate an APA-style Table
import_item_data(): Import Item Data
import_redcap_data(): Import REDCap Data
list_algorithms(): Prepare a List of Algorithms
make_dendrogram(): Make a Dendrogram of PDD Algorithms
perform_varsel(): Perform variable selection
plot_correlation(): Plot correlation matrixes
prepare_data(): Pre-process Data for Analysis
prepare_defaults(): Prepare Defaults for Data Simulation
rates: Algorithm Specifications and PDD Rates
regress_pdd_on_demographics(): Regress PDD via Logistic Regression
run_roc(): Run ROC analysis
run_scoring_rule_pipeline(): Run pipeline deriving scoring rule for PDD
simulate_pdd_data(): Make Fake Data
specify_algorithms(): Specify Algorithms for Probable PDD
subtract_concordance_matrixes(): Compare concordance matrixes
summarise_kappa(): Summarise Kappa Coefficients
summarise_rates(): Order and Prints Algorithms
table_algorithms(): Generate a Summary Table of Algorithms
table_estimands(): Generate a Summary Table of Estimands
table_levelII_approximations(): Summary Table of Predictors
trim_data(): Prepare complete data