gfpop: Graph-Constrained Functional Pruning Optimal Partitioning

Penalized parametric change-point detection by functional pruning dynamic programming algorithm. The successive means are constrained using a graph structure with edges of types null, up, down, std or abs. To each edge we can associate some additional properties: a minimal gap size, a penalty, some robust parameters (K,a). The user can also constrain the inferred means to lie between some minimal and maximal values. Data is modeled by a quadratic cost with possible use of a robust loss, biweight and Huber (see edge parameters K and a). Other losses are also available with log-linear representation or a log-log representation.

Getting started

Package details

AuthorVincent Runge [aut, cre], Toby Hocking [aut], Guillem Rigaill [aut], Daniel Grose [aut], Gaetano Romano [aut], Fatemeh Afghah [aut], Paul Fearnhead [aut], Michel Koskas [ctb], Arnaud Liehrmann [ctb]
MaintainerVincent Runge <vincent.runge@univ-evry.fr>
LicenseMIT + file LICENSE
Version1.0.3
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("gfpop")

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gfpop documentation built on Feb. 17, 2021, 5:08 p.m.