gfpop: an R Package for multiple change-point detection constrained by a graph
vendredi 25 septembre 2020, 9h30 - 10h30
The accurate detection of multiple change-points in univariate time series is a daily task for many engineers and scientists. Often, practitioners can have prior knowledge about the type of changes they are looking for. For example in genomic data, biologists expect peaks: up changes followed by down changes. Integrating such priors is important and requires dedicated algorithms.
We propose with the gfpop R package a generic algorithm able to deal with many priors constraining the successive segment means. This algorithm can be viewed as a Hidden Markov Chain model with continuous state space. gfpop works for a user-defined graph and several loss functions: Gauss, Poisson, Binomial, Biweight and Huber.
We present many priors that can be build in gfpop, they are encoded into a graph of states and constraints. The penalized optimization problem is solved by the functional pruning optimal partitioning (fpop) algorithm. This dynamic programming approach returns the exact minimizer. The approximate binary segmentation method can not be used with constraints on successive segments. We illustrate the use of gfpop on isotonic simulations and several applications in biology.