redist.smc_is_ci

Builds a confidence interval for a quantity of interest,
given importance sampling weights.

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.smc_is_ci function

<dl><dt>x</dt>
<dd>A numeric vector containing the quantity of interest</dd>
<dt>wgt</dt>
<dd>A numeric vector containing the nonnegative importance weights.
Will be normalized automatically.</dd>
<dt>conf</dt>
<dd>The confidence level for the interval.</dd></dl>

Arguments

(Deprecated) Confidence Intervals for Importance Sampling Estimates — redist.smc_is_ci

<dl>

<dt>x</dt>
<dd>A numeric vector containing the quantity of interest</dd>


<dt>wgt</dt>
<dd>A numeric vector containing the nonnegative importance weights.
Will be normalized automatically.</dd>


<dt>conf</dt>
<dd>The confidence level for the interval.</dd>

</dl>

redist.smc_is_ci: (Deprecated) Confidence Intervals for Importance Sampling Estimates

Description

Usage

Value

Arguments