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://http://imai.princeton.edu/research/files/redist.pdf>.
|Author||Ben Fifield <email@example.com>, Alexander Tarr <firstname.lastname@example.org>, Michael Higgins <email@example.com>, and Kosuke Imai <firstname.lastname@example.org>|
|Date of publication||2016-12-21 23:29:50|
|Maintainer||Ben Fifield <email@example.com>|
|License||GPL (>= 2)|
algdat.p10: All Partitions of 25 Precincts into 3 Congressional Districts...
algdat.p20: All Partitions of 25 Precincts into 3 Congressional Districts...
algdat.pfull: All Partitions of 25 Precincts into 3 Congressional Districts...
redist.combine: Combine successive runs of 'redist.mcmc'
redist.combine.mpi: Combine successive runs of 'redist.mcmc.mpi'
redist.diagplot: Diagnostic plotting functionality for MCMC redistricting.
redist.enumerate: Exact Redistricting Plan Enumerator
redist.findparams: Run parameter testing for 'redist.mcmc'
redist.ipw: Inverse probability reweighting for MCMC Redistricting
redist.mcmc: MCMC Redistricting Simulator
redist.mcmc.mpi: MCMC Redistricting Simulator Using MPI
redist-package: R Package for the MCMC Redistricting Simulator
redist.rsg: Redistricting via Random Seed and Grow Algorithm
redist.segcalc: Segregation index calculation for MCMC redistricting.