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.
Version: | 1.0.2 |
Depends: | R (≥ 3.5.0) |
Imports: | Rcpp (≥ 1.0.0) |
LinkingTo: | Rcpp |
Published: | 2020-12-01 |
Author: | Vincent Runge [aut, cre], Toby Hocking [aut], Guillem Rigaill [aut], Gaetano Romano [aut], Fatemeh Afghah [aut], Paul Fearnhead [aut], Michel Koskas [ctb], Arnaud Liehrmann [ctb] |
Maintainer: | Vincent Runge <vincent.runge at univ-evry.fr> |
License: | MIT + file LICENSE |
NeedsCompilation: | yes |
SystemRequirements: | C++11 |
CRAN checks: | gfpop results |
Reference manual: | gfpop.pdf |
Package source: | gfpop_1.0.2.tar.gz |
Windows binaries: | r-devel: gfpop_1.0.2.zip, r-release: gfpop_1.0.2.zip, r-oldrel: gfpop_1.0.2.zip |
macOS binaries: | r-release: gfpop_1.0.2.tgz, r-oldrel: gfpop_1.0.2.tgz |
Old sources: | gfpop archive |
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