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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