prec_cooccurrence

a <a rd-options="" href="/link/redist_plans?package=redist&version=3.0.7" data-mini-rdoc="redist::redist_plans">redist_plans</a> object.

plans

<code><a rd-options="dplyr:dplyr_data_masking" href="/link/%26lt%3Bdata-masking%26gt%3B?package=redist&version=3.0.7&to=dplyr%3Adplyr_data_masking" data-mini-rdoc="dplyr:dplyr_data_masking::&lt;data-masking&gt;">&lt;data-masking&gt;</a></code> which plans to
compute the co-occurrence over. Defaults to all.

which

if <code>TRUE</code>, do not include reference plans.

sampled_only

For a map with <code>n</code> precincts Returns an <code>n</code>-by-<code>n</code> matrix, where each
entry measures the fraction of the plans in which the row and column
precincts were in the same district.

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 methods
described in Fifield, Higgins, Imai and Tarr (2020) <doi:10.1080/10618600.2020.1739532>,
Fifield, Imai, Kawahara, and Kenny (2020) <doi:10.1080/2330443X.2020.1791773>,
and McCartan and Imai (2020) <arXiv:2008.06131>.

Christopher T. Kenny

redist

Simulation Methods for Legislative Redistricting

Cory McCartan

Ben Fifield

Kosuke Imai

Jun Kawahara

Alexander Tarr

Michael Higgins

prec_cooccurrence function

a <a rd-options='' href='redist_plans'>redist_plans</a> object.

<code><a rd-options='dplyr:dplyr_data_masking' href='&lt;data-masking&gt;'>&lt;data-masking&gt;</a></code> which plans to
compute the co-occurrence over. Defaults to all.

prec_cooccurrence: Compute a matrix of precinct co-occurrences

Description

Usage

Arguments

Value