Browse Items (9 total)

We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian mixture models. The goal of BayesMix is to provide a self-contained ecosystem to perform inference for mixture models to computer scientists, statisticians and…

Bayesian nonparametric models have proven to be successful tools for clustering and density estimation. While there exists a nourished ecosystem of implementations in R, for Python there are only a few. Here we develop a Python package called…

The study of hidden structures in data presents challenges in modern statistics and machine learning. We introduce the gips package in R, which identifies permutation subgroup symmetries in Gaussian vectors. gips serves two main purposes: Exploratory…

Knowing how individual abilities change is essential in a wide range of activities. The most widely used skill estimators in industry and academia (such as Elo and TrueSkill) propagate information in only one direction, from the past to the future,…

The R package sharp (Stability-enHanced Approaches using Resampling Procedures) provides an integrated framework for stability-enhanced variable selection, graphical modeling and clustering. In stability selection, a feature selection algorithm is…

Partitioning a data set by one or more of its attributes and computing an aggregate for each part is one of the most common operations in data analyses. There are use cases where the partitioning is determined dynamically by collapsing smaller…

ffect size indices are useful parameters that quantify the strength of association and are unaffected by sample size. There are many available effect size parameters and estimators, but it is difficult to compare effect sizes across studies as most…

Gaussian processes (GPs) are sophisticated distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. We implement two methods for scaling GP inference in Stan: First, a…

Multivariate random effects with unstructured variance-covariance matrices of large dimensions, q, can be a major challenge to estimate. In this paper, we introduce a new implementation of a reduced-rank approach to fit large dimensional multivariate…