R: Sensitivity Analysis for Irregular Assessment Times

# Sensitivity Analysis for Irregular Assessment Times

## Documentation for package ‘SensIAT’ version 0.3.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

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_withingroup_jackknife_results | Plot Estimates at Given Times for 'SensIAT_withingroup_jackknife_results' Objects
autoplot.SensIAT_within_group_model | Plot a 'SensIAT_within_group_model' Object
compute_influence_terms | Compute Influence Terms
compute_influence_terms.default | Compute Influence Terms
compute_influence_terms.SensIAT::Single-index-outcome-model | Compute Influence Terms
compute_SensIAT_expected_values | Compute Conditional Expected Values based on Outcome Model
compute_SensIAT_expected_values.glm | Compute Conditional Expected Values based on Outcome Model
compute_SensIAT_expected_values.lm | Compute Conditional Expected Values based on Outcome Model
compute_SensIAT_expected_values.negbin | Compute Conditional Expected Values based on Outcome Model
fit_SensIAT_fulldata_model | Produce fitted model for group (treatment or control)
fit_SensIAT_marginal_mean_model | Fit the Marginal Means Model
fit_SensIAT_single_index_fixed_bandwidth_model | Outcome Modeler for 'SensIAT' Single Index Model.
fit_SensIAT_single_index_fixed_coef_model | Outcome Modeler for 'SensIAT' Single Index Model.
fit_SensIAT_single_index_norm1coef_model | Single Index Model using MAVE and Optimizing Bandwidth.
fit_SensIAT_within_group_model | Produce fitted model for group (treatment or control)
jackknife | Perform Jackknife Resampling on an Object
jackknife.SensIAT_fulldata_model | Perform Jackknife Resampling on an Object
jackknife.SensIAT_within_group_model | Perform Jackknife Resampling on an Object
predict.SensIAT_fulldata_model | Give the Marginal Mean Estimate and its Estimated Asymptotic Variance
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
SensIAT_example_data | SensIAT Example Data
SensIAT_example_fulldata | SensIAT Example Data