oso-nextbridge.r-universe.dev

[oso-nextbridge] BayesMallowsSMC2 0.3.0

oystein.sorensen.1985@gmail.com (Oystein Sorensen) — Fri, 08 May 2026 08:02:10 GMT

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) .

[ocbe-uio] BayesMallows 2.2.7.9000

oystein.sorensen.1985@gmail.com (Oystein Sorensen) — Fri, 06 Feb 2026 11:59:32 GMT

An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 ; Crispino et al., Annals of Applied Statistics, 2019 ; Sorensen et al., R Journal, 2020 ; Stein, PhD Thesis, 2023 ). Both Metropolis-Hastings and sequential Monte Carlo algorithms for estimating the models are available. 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 ).

[lcbc-uio] BayesianLaterality 0.1.2

oystein.sorensen.1985@gmail.com (Oystein Sorensen) — Sat, 19 Aug 2023 11:00:37 GMT

Functional differences between the cerebral hemispheres are a fundamental characteristic of the human brain. Researchers interested in studying these differences often infer underlying hemispheric dominance for a certain function (e.g., language) from laterality indices calculated from observed performance or brain activation measures . However, any inference from observed measures to latent (unobserved) classes has to consider the prior probability of class membership in the population. The provided functions implement a Bayesian model for predicting hemispheric dominance from observed laterality indices (Sorensen and Westerhausen, Laterality: Asymmetries of Body, Brain and Cognition, 2020, ).

[osorensen] hdme 0.6.0

oystein.sorensen.1985@gmail.com (Oystein Sorensen) — Tue, 16 May 2023 18:51:20 GMT

Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) ). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) ).