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BayesMallowsSMC2: Nested Sequential Monte Carlo for the Bayesian Mallows Model

Provides nested sequential Monte Carlo algorithms for performing sequential inference in the Bayesian Mallows model, which is a widely used probability model for rank and preference data. The package implements the SMC2 (Sequential Monte Carlo Squared) algorithm for handling sequentially arriving rankings and pairwise preferences, including support for complete rankings, partial rankings, and pairwise comparisons. The methods are based on Sorensen (2025) <doi:10.1214/25-BA1564>.

Version: 0.2.0
Depends: R (≥ 4.1.0)
Imports: Rcpp, ggplot2, Rdpack
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (≥ 3.0.0), label.switching (≥ 1.8)
Published: 2026-02-03
DOI: 10.32614/CRAN.package.BayesMallowsSMC2 (may not be active yet)
Author: Oystein Sorensen ORCID iD [aut, cre]
Maintainer: Oystein Sorensen <oystein.sorensen.1985 at gmail.com>
License: GPL-3
NeedsCompilation: yes
Materials: README, NEWS
CRAN checks: BayesMallowsSMC2 results

Documentation:

Reference manual: BayesMallowsSMC2.html , BayesMallowsSMC2.pdf

Downloads:

Package source: BayesMallowsSMC2_0.2.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): BayesMallowsSMC2_0.2.0.tgz, r-oldrel (x86_64): BayesMallowsSMC2_0.2.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.