redist.random.subgraph

<code>random.subgraph</code> returns a random subset of the shp provided

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.random.subgraph function

<dl><dt>shp</dt>
<dd>sf object or SpatialPolygonsDataFrame</dd>
<dt>n</dt>
<dd>number of edges to sample. n must be a positive integer.</dd>
<dt>adj</dt>
<dd>Optional. zero indexed adjacency list.</dd></dl>

Arguments

Return a random subgraph of a shape — redist.random.subgraph

<dl>

<dt>shp</dt>
<dd>sf object or SpatialPolygonsDataFrame</dd>


<dt>n</dt>
<dd>number of edges to sample. n must be a positive integer.</dd>


<dt>adj</dt>
<dd>Optional. zero indexed adjacency list.</dd>

</dl>

redist.random.subgraph: Return a random subgraph of a shape

Description

Usage

Value

Arguments

Details