BayesMallowsSMC2/DESCRIPTION at db9d793ec565d2d53d3e718e0f9d87f781e9adc1 · osorensen/BayesMallowsSMC2 · GitHub

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Package: BayesMallowsSMC2
Type: Package
Title: Nested Sequential Monte Carlo for the Bayesian Mallows Model
Version: 0.1.1
Authors@R: c(person("Oystein", "Sorensen",
                    email = "oystein.sorensen.1985@gmail.com",
                    role = c("aut", "cre"),
                    comment = c(ORCID = "0000-0003-0724-3542")))
Maintainer: Oystein Sorensen <oystein.sorensen.1985@gmail.com>
Description: Implements nested sequential Monte Carlo (SMC) algorithms for
    Bayesian inference in the Mallows model for ranking data. The package
    provides efficient computational methods for analyzing preference rankings
    and partial rankings using particle filtering techniques. The algorithms
    handle time-varying ranking data and can accommodate various distance
    metrics including Kendall's tau, Spearman, footrule, Cayley, Hamming,
    and Ulam distances. Based on the methodology described in
    <doi:10.1214/25-BA1564>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
LinkingTo:
    Rcpp,
    RcppArmadillo
Imports:
    Rcpp
Depends:
    R (>= 4.1.0)
Suggests:
    testthat (>= 3.0.0),
    label.switching (>= 1.8)
Config/testthat/edition: 3