Man pages for simITS
Analysis via Simulation of Interrupted Time Series (ITS) Data
| add_lagged_covariates | Augment dataframe with lagged covariates |
| adjust_data | Adjust an outcome time series based on the group weights. |
| aggregate_data | Aggregate grouped data |
| aggregate_simulation_results | Test a passed test statistic on the simulated data |
| calculate_average_outcome | Summary function for summarize.simulation.results |
| calculate_group_weights | Calculate proportion of subgroups across time |
| extrapolate_model | Extrapolate pre-policy data to post-policy era |
| fit_model_default | Default ITS model |
| generate_fake_data | Make fake data for testing purposes. |
| generate_fake_grouped_data | A fake DGP with time varying categorical covariate for... |
| make_envelope_graph | Make envelope style graph with associated smoothed trendlines |
| make_fit_season_model | Make a fit_model that takes a seasonality component |
| make_many_predictions | Generate a collection of raw counterfactual trajectories |
| make_model_smoother | Make a smoother that fits a model and then smooths residuals |
| mecklenberg | Mecklenberg PSA Reform Data |
| meck_subgroup | Mecklenberg data by subgroup of charge type |
| newjersey | New Jersey PSA Reform aggregate data |
| process_outcome_model | Generate an ITS extrapolation simulation. |
| simITS | 'simITS' package overview |
| smooth_residuals | Smooth residuals after model fit |
| smooth_series | Smooth a series using a static loess smoother |
