<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Rankings on Øystein Sørensen</title><link>https://osorensen.no/tags/rankings/</link><description>Recent content in Rankings on Øystein Sørensen</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 28 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://osorensen.no/tags/rankings/index.xml" rel="self" type="application/rss+xml"/><item><title>BayesMallowsSMC2: Nested Sequential Monte Carlo for the Bayesian Mallows Model</title><link>https://osorensen.no/software/software6/</link><pubDate>Fri, 08 May 2026 00:00:00 +0000</pubDate><guid>https://osorensen.no/software/software6/</guid><description>This R package provides nested sequential Monte Carlo algorithms for performing sequential inference in the Bayesian Mallows model.</description></item><item><title>BayesMallows: An R Package for the Bayesian Mallows Model</title><link>https://osorensen.no/papers/paper5/</link><pubDate>Mon, 01 Jun 2020 00:00:00 +0000</pubDate><guid>https://osorensen.no/papers/paper5/</guid><description>This paper presents the BayesMallows R package, for analysis of rank and preference data. Published in the R Journal.</description></item><item><title>BayesMallows: Bayesian Preference Learning with the Mallows Rank Model</title><link>https://osorensen.no/software/software2/</link><pubDate>Mon, 01 Oct 2018 00:00:00 +0000</pubDate><guid>https://osorensen.no/software/software2/</guid><description>This R package contains functions for estimating the Bayesian Mallows model in a wide range of situation, using the Metropolis-Hastings algorithm.</description></item></channel></rss>