Ben Fifield <bfifield@princeton.edu>, Alexander Tarr
<atarr@princeton.edu>, Michael Higgins <mikehiggins@k-state.edu>, and Kosuke
Imai <kimai@princeton.edu>
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>.
Rcpp (>= 0.11.0), spdep, sp, coda, parallel, doParallel,
foreach

Ben Fifield <bfifield@princeton.edu>, Alexander Tarr
<atarr@princeton.edu>, Michael Higgins <mikehiggins@k-state.edu>, and Kosuke
Imai <imai@harvard.edu>
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
<https://imai.fas.harvard.edu/research/files/redist.pdf>.

Ben Fifield <bfifield@princeton.edu>, Alexander Tarr <atarr@princeton.edu>, Michael Higgins <mikehiggins@k-state.edu>, and Kosuke Imai <kimai@princeton.edu>
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>.