Title: Bayesian Multi-Resolution Gaussian Process Approximations
Authors@R: person("John", "Tipton", email = "jrtipton@uark.edu",
Description: Software for fitting sparse Bayesian multi-resolution spatial models using Markov Chain Monte Carlo.
Imports: fields, igraph, Matrix, mvnfast, Rcpp (>= 1.0.4.6), spam
Suggests: knitr, pkgdown, rmarkdown, testthat (>= 2.1.0), covr

Title: Bayesian Preference Learning with the Mallows Rank Model
Author: Oystein Sorensen, Valeria Vitelli, Marta Crispino, Qingua Liu
Description: 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>). Both Cayley, footrule, Kendall, and Spearman distances are supported in the models. The rank data to be analyzed can be the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent 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>).
Suggests: testthat, gtools (>= 3.8.1), knitr, PerMallows (>= 1.13),

Author: Oystein Sorensen, Valeria Vitelli, Marta Crispino, Qinghua Liu

Suggests: testthat, gtools (>= 3.8.1), knitr, rmarkdown, PerMallows (>=

Description: 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>).
Suggests: testthat, gtools (>= 3.8.1), knitr, rmarkdown, covr, parallel