calculate_weights       Calculate Inverse Probability of Censoring
                        Weights
case_control_sampling_trials
                        Case-control sampling of expanded data for the
                        sequence of emulated trials
data_censored           Example of longitudinal data for sequential
                        trial emulation containing censoring
data_preparation        Prepare data for the sequence of emulated
                        target trials
expand_trials           Expand trials
fit_msm                 Fit the marginal structural model for the
                        sequence of emulated trials
fit_weights_model       Method for fitting weight models
initiators              A wrapper function to perform data preparation
                        and model fitting in a sequence of emulated
                        target trials
ipw_data                IPW Data Accessor and Setter
load_expanded_data      Method to read, subset and sample expanded data
outcome_data            Outcome Data Accessor and Setter
parsnip_model           Fit outcome models using 'parsnip' models
predict_marginal        Predict marginal cumulative incidences with
                        confidence intervals for a target trial
                        population
print.TE_weight_summary
                        Print a weight summary object
read_expanded_data      Method to read expanded data
sample_expanded_data    Internal method to sample expanded data
save_expanded_data      Method to save expanded data
save_to_csv             Save expanded data as CSV
save_to_datatable       Save expanded data as a 'data.table'
save_to_duckdb          Save expanded data to 'DuckDB'
set_censor_weight_model
                        Set censoring weight model
set_data                Set the trial data
set_expansion_options   Set expansion options
set_outcome_model       Specify the outcome model
set_switch_weight_model
                        Set switching weight model
show_weight_models      Show Weight Model Summaries
stats_glm_logit         Fit outcome models using 'stats::glm'
summary.TE_data_prep    Summary methods
te_data-class           TrialEmulation Data Class
te_data_ex              Example of a prepared data object
te_datastore-class      te_datastore
te_model_ex             Example of a fitted marginal structural model
                        object
te_model_fitter-class   Outcome Model Fitter Class
te_outcome_data-class   TrialEmulation Outcome Data Class
te_outcome_fitted-class
                        Fitted Outcome Model Object
te_outcome_model-class
                        Fitted Outcome Model Object
trial_example           Example of longitudinal data for sequential
                        trial emulation
trial_msm               Fit the marginal structural model for the
                        sequence of emulated trials
trial_sequence          Create a sequence of emulated target trials
                        object
trial_sequence-class    Trial Sequence class
vignette_switch_data    Example of expanded longitudinal data for
                        sequential trial emulation
weight_model_data_indices
                        Data used in weight model fitting
