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://imai.fas.harvard.edu/research/files/redist.pdf>.
| Package: | redist |
| Type: | Package |
| Version: | 2.0.2 |
| Date: | 2020-10-03 |
| License: | GPL (>= 2) |
Barbu, Adrian and Song-Chun Zhu. (2005) "Generalizing Swendsen-Wang to Sampling Arbitrary Posterior Probabilities." IEEE Transactions on Pattern Analysis and Machine Intelligence.
Fifield, Benjamin, Michael Higgins, Kosuke Imai and Alexander Tarr. (2016) "A New Automated Redistricting Simulator Using Markov Chain Monte Carlo." Working Paper. Available at http://imai.princeton.edu/research/files/redist.pdf.
Swendsen, Robert and Jian-Sheng Wang. (1987) "Nonuniversal Critical Dynamics in Monte Carlo Simulations." Physical Review Letters.