Package index • SensIAT

All functions

SensIAT_example_data SensIAT_example_fulldata: SensIAT Example Data
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_bootstrap_results>): Plot Bootstrap Marginal Mean Curves with Confidence Bands
autoplot(<SensIAT_withingroup_jackknife_results>): Plot Estimates at Given Times for SensIAT_withingroup_jackknife_results Objects
benchmark_term2_methods(): Benchmark Term2 Integration Methods
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_marginal_mean_model_generalized(): Fit the marginal mean model for generalize outcomes.
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)
fit_marginal_model(): Fit SensIAT Marginal Mean Model (Unified)
get_bootstrap_coeffs(): Get coefficients for bootstrap simulation.
jackknife(): Perform Jackknife Resampling on an Object
make_parametric_intensity_simulator(): Build a parametric intensity simulator using sampled coefficients
make_parametric_single_index_simulator(): Build a parametric outcome simulator from a fitted single-index outcome model using sampled coefficients for the single-index projection.
make_single_index_simulator(): Create a simulator function from a fitted Single-index outcome model
parametric_bootstrap() experimental: Parametric bootstrap orchestration
parametric_bootstrap_within_group() experimental: Parametric bootstrap for a within-group SensIAT model
predict(<SensIAT_fulldata_model>) predict(<SensIAT_within_group_model>): Give the Marginal Mean Estimate and its Estimated Asymptotic Variance
predict(<SensIAT_withingroup_bootstrap_results>): Predict Marginal Mean from Bootstrap Coefficient Replicates
prepare_SensIAT_data(): Prepare Data for Sensitivity Analysis with Irregular Assessment Times
sample_parametric_coeffs(): Sample parametric coefficients from a fitted model using asymptotic multivariate normal distribution.
simulate_SensIAT_data(): Simulate SensIAT Data
simulate_SensIAT_two_groups(): Simulate Treatment and Control Groups