Package: simITS 0.1.1

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

Uses simulation to create prediction intervals for post-policy outcomes in interrupted time series (ITS) designs, following Miratrix (2020) <arxiv:2002.05746>. This package provides methods for fitting ITS models with lagged outcomes and variables to account for temporal dependencies. It then conducts inference via simulation, simulating a set of plausible counterfactual post-policy series to compare to the observed post-policy series. This package also provides methods to visualize such data, and also to incorporate seasonality models and smoothing and aggregation/summarization. This work partially funded by Arnold Ventures in collaboration with MDRC.

Authors:Luke Miratrix [aut, cre], Brit Henderson [ctb], Chloe Anderson [ctb], Arnold Ventures [fnd], MDRC [fnd]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
simITS/json (API)

# Install 'simITS' in R:

install.packages('simITS', repos = c('https://lmiratrix.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/lmiratrix/simits/issues

Datasets:

meck_subgroup - Mecklenberg data by subgroup of charge type
mecklenberg - Mecklenberg PSA Reform Data
newjersey - New Jersey PSA Reform aggregate data

On CRAN:

4.30 score 2 stars 219 downloads 18 exports 15 dependencies

Last updated from:6a4c3cb91f. Checks:7 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	ERROR	143
source / vignettes	OK	228
linux-release-x86_64	ERROR	158
macos-release-arm64	ERROR	108
macos-oldrel-arm64	ERROR	161
windows-devel	ERROR	93
windows-release	ERROR	90
windows-oldrel	ERROR	92
wasm-release	OK	107

Exports:add_lagged_covariates adjust_data aggregate_data aggregate_simulation_results calculate_average_outcome calculate_group_weights extrapolate_model fit_model_default generate_fake_data generate_fake_grouped_data make_envelope_graph make_fit_season_model make_many_predictions make_many_predictions_plug make_model_smoother process_outcome_model smooth_residuals smooth_series

Dependencies:cli dplyr generics glue lifecycle magrittr pillar pkgconfig R6 rlang tibble tidyselect utf8 vctrs withr

Introduction to the simITS package

Rendered fromsimple_ITS_example.Rmdusingknitr::rmarkdown

Last update: 2023-09-01
Started: 2020-04-07

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

Package: simITS 0.1.1

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

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Usage by other packages (reverse dependencies)