redist Recieved POLMETH’s 2022 Statistical Software Award

Our software won the Society for Political Methodology’s Statistical Software Award.

Christopher T. Kenny , Cory McCartan , Ben Fifield , Kosuke Imai

Last week, Chris Kenny, Cory McCartan, Ben Fifield, and Kosuke Imai received this year’s Statistical Software Award from the Society for Political Methodology for the R package redist.

The announcement stated:

The redist package by Christopher T Kenny, Cory McCartan, Ben Fifield, and Kosuke Imai of the Algorithm-Assisted Redistricting Methodology Project has rapidly become a key resource for researchers and practitioners seeking to evaluate redistricting plans. redist develops statistically grounded and computationally efficient procedures for generating random draws from a distribution of viable redistricting plans, including conditional distributions that satisfy specified requirements for geographic compactness and population parity. The package allows users to test for illegal partisan and racial gerrymandering, a timely and important question in the wake of the 2020 Census and the redistricting cycle that followed. It has had a substantial policy impact seeing use in legal challenges against and, unusually, has also been cited by six Supreme Court justices in oral arguments. In short, it is an ideal recipient of the Society for Political Methodolgy’s 2022 Statistical Software Award.

We are grateful for the honor! Thank you to the award committee for considering our software.

redist is an open source R package for sampling redistricting plans, available here:

This R package enables researchers to sample redistricting plans from a pre-specified target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo algorithms. The package supports various constraints in the redistricting process, such as geographic compactness and population parity requirements. Tools for analysis, including computation of various summary statistics and plotting functionality, are also included.

redist is a key tool for much of our work, which has enabled work on identifying bias in Census 2020, creating alternative plans for all 50 states, reducing malapportionment in Japan, assessing partisan bias in 2022’s congressional districts, and more.


For attribution, please cite this work as

Kenny, et al. (2022, Dec. 15). ALARM Project: redist Recieved POLMETH's 2022 Statistical Software Award. Retrieved from

BibTeX citation

  author = {Kenny, Christopher T. and McCartan, Cory and Fifield, Ben and Imai, Kosuke},
  title = {ALARM Project: redist Recieved POLMETH's 2022 Statistical Software Award},
  url = {},
  year = {2022}