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

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

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

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.

Vincent Runge

https://www.jstatsoft.org/article/view/v106i06

Université d’Évry

March 2023

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