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

Vincent Runge, Toby Dylan Hocking, Gaetano Romano, Fatemeh Afghah, Paul Fearnhead, Guillem Rigaill

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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, Rigaill, Fearnhead, and Bourque (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 changes 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 105 data points.

Original languageEnglish (US)
JournalJournal of Statistical Software
Volume106
DOIs
StatePublished - 2023

Keywords

  • change-point detection
  • constrained inference
  • dynamic programming
  • maximum likelihood inference
  • robust losses

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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