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.


Reference manual

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1.0.3 by Vincent Runge, a year ago

Browse source code at

Authors: 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]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports Rcpp

Linking to Rcpp

System requirements: C++11

See at CRAN