Package index • SensIAT

All functions

SensIAT_example_data SensIAT_example_fulldata: SensIAT Example Data

SensIAT_fit_marginal_model(): Title

SensIAT_jackknife() SensIAT_jackknife_fulldata(): Estimate response with jackknife resampling

SensIAT_prepare_data(): Prepare data for SensIAT analysis

SensIAT_sim_outcome_modeler() SensIAT_sim_outcome_modeler_fbw(): Outcome Modeler for SensIAT Single Index Model.

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

add_class(): Adds an S3 class to an object

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_influence_terms(): Compute Influence Terms

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

jackknife(): Perform Jackknife resampling on an object.

pcoriaccel_estimate_pmf(): Directly estimate the probability mass function of Y.

pcoriaccel_evaluate_basis(): Compiled version of evaluate_basis() function

pcoriaccel_evaluate_basis_mat(): Compiled version of evaluate_basis() function (matrix version)

predict(<SensIAT_fulldata_model>) predict(<SensIAT_within_group_model>): Predict mean and variance of the outcome for a SensIAT within-group model

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