gfpop: an R Package for Univariate Graph-Constrained Change-Point Detection

TL;DR

gfpop is an R package that implements a flexible, graph-based algorithm for detecting change-points in large univariate data sequences, incorporating prior knowledge to improve accuracy across various applications.

Contribution

It introduces a generic implementation of a graph-constrained change-point detection algorithm in R/C++, allowing customizable prior assumptions and multiple loss functions.

Findings

Efficient detection of change-points in large datasets within seconds to minutes.

Flexible modeling of prior knowledge through user-defined graphs.

Application demonstrated in biological data analysis.

Abstract

In a world with data that change rapidly and abruptly, it is important to detect those changes accurately. In this paper we describe an R package implementing a generalized version of an algorithm recently proposed by Hocking et al. [2020] for penalized maximum likelihood inference of constrained multiple change-point models. This algorithm can be used to pinpoint the precise locations of abrupt changes in large data sequences. There are many application domains for such models, such as medicine, neuroscience or genomics. Often, practitioners have prior knowledge about the changes they are looking for. For example in genomic data, biologists sometimes expect peaks: up changes followed by down changes. Taking advantage of such prior information can substantially improve the accuracy with which we can detect and estimate changes. Hocking et al. [2020] described a graph framework to encode many examples of such prior information and a generic algorithm to infer the optimal model parameters, but implemented the algorithm for just a single scenario. We present the gfpop package that implements the algorithm in a generic manner in R/C++. gfpop works for a user-defined graph that can encode prior assumptions about the types of change that are possible and implements several loss functions (Gauss, Poisson, binomial, biweight and Huber). We then illustrate the use of gfpop on isotonic simulations and several applications in biology. For a number of graphs the algorithm runs in a matter of seconds or minutes for 10^5 data points.