R: Analysis via Simulation of Interrupted Time Series (ITS) Data

# Analysis via Simulation of Interrupted Time Series (ITS) Data

## Documentation for package ‘simITS’ version 0.1.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

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 illustrating the code.
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_many_predictions_plug | 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