Package: gfpop
Type: Package
Title: Graph-Constrained Functional Pruning Optimal Partitioning
Version: 1.1.2
Authors@R: c(
	person("Vincent", "Runge", email = "vincent.runge@univ-evry.fr", role = c("aut", "cre")),
	person("Toby", "Hocking", role = c("aut")),
	person("Guillem", "Rigaill", role = c("aut")),
	person("Daniel", "Grose", role = c("aut")),
	person("Gaetano", "Romano", role = c("aut")),
	person("Fatemeh", "Afghah", role = c("aut")),
	person("Paul", "Fearnhead", role = c("aut")),
	person("Michel", "Koskas", role = c("ctb")),
	person("Arnaud", "Liehrmann", role = c("ctb")))
Maintainer: Vincent Runge <vincent.runge@univ-evry.fr>
Description: Penalized parametric change-point detection by functional
        pruning dynamic programming algorithm. The successive means are
        constrained using a graph structure with edges defining the
        nature of the changes These changes can be unconstrained (type
        std), up or down constrained (type up and down) or constrained
        by a minimal size jump (type abs). The type null means that the
        graph allows us to stay on the same segment. To each edge we
        can associate some additional properties: a minimal gap size, a
        penalty, some robust parameters (K,a) for biweight (K) and
        Huber losses (K and a). The user can also constrain the
        inferred means to lie between some minimal and maximal values.
        Data is modeled by a cost with possible use of a robust loss,
        biweight and Huber (see edge parameters K and a). These costs
        should have a quadratic, log-linear or a log-log
        representation. This includes quadratic Gaussian cost (type =
        'mean'), log-linear cost (type = 'variance', 'poisson' or
        'exp') and log-log cost (type = 'negbin'). More details in the
        paper published in the Journal of Statistical Software:
        <doi:10.18637/jss.v106.i06>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 1.0.0)
LinkingTo: Rcpp
Suggests: knitr, data.table, testthat, rmarkdown, ggplot2,
        penaltyLearning
VignetteBuilder: knitr
Depends: R (>= 3.5.0)
Config/roxygen2/version: 8.1.0
NeedsCompilation: yes
Packaged: 2026-09-27 14:18:05 UTC; root
Author: Vincent 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]
Repository: https://cran.r-universe.dev
Date/Publication: 2026-09-27 09:10:21 UTC
RemoteUrl: https://github.com/cran/gfpop
RemoteRef: HEAD
RemoteSha: 401bd8a1c18df1f52d9dfc1d82d39a6a27747c15
