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

02/10/2020
by   Vincent Runge, et al.
0

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 an algorithm recently proposed by Hocking et al. [2017] for penalised 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. [2017] 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 the prior nformation of the types of change 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 datapoints.

READ FULL TEXT

page 14

page 15

research
02/13/2021

Estimation for change point of discretely observed ergodic diffusion processes

We treat the change point problem in ergodic diffusion processes from di...
research
04/23/2021

Change point inference in ergodic diffusion processes based on high frequency data

We deal with the change point problem in ergodic diffusion processes bas...
research
08/10/2021

Rank Energy Statistics in the Context of Change Point Detection

In this paper, I propose a general procedure for multivariate distributi...
research
02/17/2023

Multiple change-point detection for Poisson processes

Change-point detection aims at discovering behavior changes lying behind...
research
01/02/2018

A review of change point detection methods

In this work, methods to detect one or several change points in multivar...
research
05/23/2021

Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package

Vector Auto-Regressive (VAR) models capture lead-lag temporal dynamics o...
research
05/18/2018

Change Point Methods on a Sequence of Graphs

The present paper considers a finite sequence of graphs, e.g., coming fr...

Please sign up or login with your details

Forgot password? Click here to reset