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 <>.


Reference manual

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1.3-3 by Ben Fifield, a year ago

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Authors: Ben Fifield <[email protected]> , Alexander Tarr <[email protected]> , Michael Higgins <[email protected]> , and Kosuke Imai <[email protected]>

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, spdep, sp, coda, parallel, doParallel, foreach

Suggests testthat, Rmpi

Linking to Rcpp, RcppArmadillo

System requirements: gmp, libxml2

See at CRAN