Analysis via Simulation of Interrupted Time Series (ITS) Data

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Documentation for package ‘simITS’ version 0.1.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

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