Package index • myphd

Index
`add_metadata()`	Add metadata to dataframe
`bound_outcome()`	Bound a outcome variable
`check_model()`	Tidy checks for model fit
`convert_time_season()`	Convert time variables to season
`create_df_marginal_comparisons()`	Create dataframe for marginal comparisons
`create_fake_data()`	Create a fake dataframe based on a real one
`create_formula()`	Programmatic way of constructing model formulas
`create_mapping_labels()`	Create a variable's dictionary for its labels
`describe_data()`	A function to describe the used dataframe
`edcs_information()`	Return information on chemicals of interest
`estimate_selection_weights()`	Title
`estimate_weights()`	Wrapper function to estimate balancing weights
`explore_balance()`	Assess balance on covariate distributions generated through weighting
`explore_missings()`	A function to explore the missing values of a dataframe
`explore_shift()`	Visualize the distribution of the shifted exposures
`extract_cohort()`	Extract the cohort label from the subject ID
`find_common_confounders()`	Find common confounders
`fit_model_weighted()`	Fit various models with weighting
`from_dagitty_to_ggdag()`	Convert a DAG to a formatted string
`geom_dag_text_repel()`	Repulsive textual annotations
`handle_creatinine_confounding()`	Title
`handle_llodq()`	Various strategies to handle values below the limit of detection/quantification
`handle_missing_values()`	Various strategies to handle missing values
`handle_standardization()`	Title
`handle_transformation()`	Title
`load_ctd()`	Load local CTD file
`minimize_missings()`	Select confounders to minimize missing values
`plot_adrf()`	Title
`plot_amef()`	Title
`plot_ctd()`	Visualize the CTD dataset after loading
`plot_effect_estimates()`	Plot effect estimates with confidence intervals
`preproc_data()`	Basic pre-processing of dataframes
`test_npsem()`	Wrapper function to derive a DAG's testable implications
`theme_dag()`	Minimalist DAG theme
`visualize_dag()`	Visualize DAG

Reference

Index