redist.plot.varinfo

Static Variation of Information Plot

Enables researchers to sample redistricting plans from a pre-specified
target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo
algorithms. The package allows for the implementation of various constraints in
the redistricting process such as geographic compactness and population parity
requirements. Tools for analysis such as computation of various summary statistics
and plotting functionality are also included. The package implements the SMC
algorithm of McCartan and Imai (2023) <doi:10.1214/23-AOAS1763>, the enumeration
algorithm of Fifield, Imai, Kawahara, and Kenny (2020) <doi:10.1080/2330443X.2020.1791773>,
the Flip MCMC algorithm of Fifield, Higgins, Imai and Tarr (2020) <doi:10.1080/10618600.2020.1739532>,
the Merge-split/Recombination algorithms of Carter et al. (2019) <arXiv:1911.01503>
and DeFord et al. (2021) <doi:10.1162/99608f92.eb30390f>, and the Short-burst
optimization algorithm of Cannon et al. (2020) <arXiv:2011.02288>.

Christopher T. Kenny

redist

Simulation Methods for Legislative Redistricting

Cory McCartan

Ben Fifield

Kosuke Imai

Jun Kawahara

Alexander Tarr

Michael Higgins

redist.plot.varinfo function

<dl><dt>plans</dt>
<dd>matrix of district assignments</dd>
<dt>group_pop</dt>
<dd>Required Population of subgroup being studied in each precinct.</dd>
<dt>total_pop</dt>
<dd>Required. Population of each precinct.</dd>
<dt>shp</dt>
<dd>sf dataframe</dd></dl>

Arguments

Static Variation of Information Plot — redist.plot.varinfo

<dl>

<dt>plans</dt>
<dd>matrix of district assignments</dd>


<dt>group_pop</dt>
<dd>Required Population of subgroup being studied in each precinct.</dd>


<dt>total_pop</dt>
<dd>Required. Population of each precinct.</dd>


<dt>shp</dt>
<dd>sf dataframe</dd>

</dl>

redist.plot.varinfo: Static Variation of Information Plot

Description

Usage

Value

Arguments