redist: Markov Chain Monte Carlo Methods for Redistricting Simulation

Enables researchers to sample redistricting plans from a pre- specified target distribution using a Markov Chain Monte Carlo algorithm. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements. The algorithm also can be used in combination with efficient simulation methods such as simulated and parallel tempering algorithms. Tools for analysis such as inverse probability reweighting and plotting functionality are included. The package implements methods described in Fifield, Higgins, Imai and Tarr (2016) “A New Automated Redistricting Simulator Using Markov Chain Monte Carlo,” working paper available at <http:// research/files/redist.pdf>.

Version: 1.3-1
Depends: R (≥ 3.1.0)
Imports: Rcpp (≥ 0.11.0), spdep, sp, coda, parallel, doParallel, foreach
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, Rmpi
Published: 2017-03-15
Author: Ben Fifield, Alexander Tarr, Michael Higgins, and Kosuke Imai
Maintainer: Ben Fifield <bfifield at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: gmp, libxml2
Citation: redist citation info
Materials: ChangeLog
CRAN checks: redist results


Reference manual: redist.pdf
Package source: redist_1.3-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: redist_1.3-1.tgz, r-oldrel: redist_1.3-1.tgz
Old sources: redist archive


Please use the canonical form to link to this page.