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arXiv · 2607.19961

focus and focus-cpt: Fast Online Changepoint Detection in R and Python

Gaetano Romano·Kes Ward·Yuntang Fan·Guillem Rigaill·Vincent Runge·Idris A. Eckley·Paul Fearnhead
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Abstract

We present an R and Python package for fast online changepoint detection in univariate and multivariate data streams for a variety of models. The package implements the focus family of algorithms, which compute the Generalised Likelihood Ratio test for a single changepoint exactly and efficiently, with a per-iteration cost of approximately $\log(n)^d$ for a d-dimensional sequence, without introducing approximations. This is achieved by exploiting a connection between the location of the changepoint candidates and the geometry of the data. The package supports a broad range of models from the natural exponential family, including Gaussian, Poisson, Binomial, Exponential and Gamma distributions, as well as a non-parametric detector based on the empirical cumulative distribution function and a detector for autoregressive data.

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Gaetano Romano, Kes Ward, Yuntang Fan, Guillem Rigaill, Vincent Runge, Idris A. Eckley, Paul Fearnhead. 2026-07-22. focus and focus-cpt: Fast Online Changepoint Detection in R and Python. https://arxiv.org/abs/2607.19961

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