Package index • SensIAT

All functions

SensIAT_example_data SensIAT_example_fulldata: SensIAT Example Data

add_terminal_observations(): Add Terminal Observations to a Dataset

autoplot(<SensIAT_fulldata_jackknife_results>): Plot for Estimated Treatment Effect for SensIAT_fulldata_jackknife_results Objects

autoplot(<SensIAT_fulldata_model>): Plot for Estimated Treatment Effect for SensIAT_fulldata_model Objects

autoplot(<SensIAT_within_group_model>): Plot a SensIAT_within_group_model Object

autoplot(<SensIAT_withingroup_jackknife_results>): Plot Estimates at Given Times for SensIAT_withingroup_jackknife_results Objects

compute_SensIAT_expected_values(): Compute Conditional Expected Values based on Outcome Model

compute_influence_terms(): Compute Influence Terms

fit_SensIAT_marginal_mean_model(): Fit the Marginal Means Model

fit_SensIAT_single_index_fixed_coef_model() fit_SensIAT_single_index_fixed_bandwidth_model(): Outcome Modeler for SensIAT Single Index Model.

fit_SensIAT_single_index_norm1coef_model(): Single Index Model using MAVE and Optimizing Bandwidth.

fit_SensIAT_fulldata_model() fit_SensIAT_within_group_model(): Produce fitted model for group (treatment or control)

jackknife(): Perform Jackknife Resampling on an Object

predict(<SensIAT_fulldata_model>) predict(<SensIAT_within_group_model>): Give the Marginal Mean Estimate and its Estimated Asymptotic Variance

prepare_SensIAT_data(): Prepare Data for Sensitivity Analysis with Irregular Assessment Times