R package available from CRAN. See also the accompanying Shiny App.
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.

BayesMallows is an R package for analyzing preference data in the form of rankings with the Mallows rank model, and its finite mixture extension, in a Bayesian framework. The model is grounded on the idea that the probability density of an observed ranking decreases exponentially with the distance to the location parameter. It is the first Bayesian implementation that allows wide choices of distances, and it works well with a large amount of items to be ranked.

R package available from CRAN.
Meta-analysis of generalized additive models and generalized additive mixed models. A typical use case is when data cannot be shared across locations, and an overall meta-analytic fit is sought. ‘metagam’ provides functionality for removing individual participant data from models computed using the ‘mgcv’ and ‘gamm4’ packages such that the model objects can be shared without exposing individual data. Furthermore, methods for meta-analysing these fits are provided.

This is the companion paper to the hdme R package. Link to paper.

In preparing for my upcoming Rcpp talk at the Oslo useR! Group, I started wondering how much of R is actually written in C or Fortran. I have of course been trained to think that vectorization is great, because then you let C or Fortran do the job, but how much of R is actually written in these languages? Some searching led me to this blog, which analyzes R-2.

R package available from CRAN.
An implementation of the Bayesian version of the Mallows rank model. 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.

R package available from CRAN.
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. 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.

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