BayesMallows

Bayesian Preference Learning with the Mallows Rank Model

An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <http://jmlr.org/papers/v18/15-481.html>; Crispino et al., to appear in Annals of Applied Statistics). Both Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).

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Description file content

Package
BayesMallows
Type
Package
Title
Bayesian Preference Learning with the Mallows Rank Model
Version
0.4.0
Author
Oystein Sorensen, Valeria Vitelli, Marta Crispino, Qinghua Liu
Maintainer
Oystein Sorensen
Description
An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 ; Crispino et al., to appear in Annals of Applied Statistics). Both Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 ).
URL
License
GPL-3
Encoding
UTF-8
LazyData
true
RoxygenNote
6.1.1
Depends
R (>= 2.10)
Imports
Rcpp (>= 1.0.0), ggplot2 (>= 3.1.0), Rdpack (>= 0.8), stats, igraph (>= 1.2.2), dplyr (>= 0.7.8), sets (>= 1.0-18), relations (>= 0.6-8), tidyr (>= 0.8.2), purrr (>= 0.3.0), rlang (>= 0.3.1), PerMallows (>= 1.13), HDInterval (>= 0.2.0), cowplot (>= 0.9.3)
LinkingTo
Rcpp, RcppArmadillo
Suggests
testthat (>= 2.0), label.switching (>= 1.7), readr (>= 1.3.1), stringr (>= 1.4.0), gtools (>= 3.8.1), knitr (>= 1.21), rmarkdown, covr, parallel (>= 3.5.1)
VignetteBuilder
knitr
RdMacros
Rdpack
NeedsCompilation
yes
Packaged
2019-02-22 13:27:54 UTC; oyss
Repository
CRAN
Date/Publication
2019-02-22 14:30:29 UTC

install.packages('BayesMallows')

0.4.0

3 months ago

https://github.com/osorensen/BayesMallows

Oystein Sorensen

GPL-3

Depends on

R (>= 2.10)

Imports

Rcpp (>= 1.0.0), ggplot2 (>= 3.1.0), Rdpack (>= 0.8), stats, igraph (>= 1.2.2), dplyr (>= 0.7.8), sets (>= 1.0-18), relations (>= 0.6-8), tidyr (>= 0.8.2), purrr (>= 0.3.0), rlang (>= 0.3.1), PerMallows (>= 1.13), HDInterval (>= 0.2.0), cowplot (>= 0.9.3)

Suggests

testthat (>= 2.0), label.switching (>= 1.7), readr (>= 1.3.1), stringr (>= 1.4.0), gtools (>= 3.8.1), knitr (>= 1.21), rmarkdown, covr, parallel (>= 3.5.1)

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