{ "_id": "6aba49d199f7aa8c6aa3fa64", "Package": "gfpop", "Type": "Package", "Title": "Graph-Constrained Functional Pruning Optimal Partitioning", "Version": "1.1.2", "Authors@R": "c(\nperson(\"Vincent\", \"Runge\", email = \"vincent.runge@univ-evry.fr\", role = c(\"aut\", \"cre\")),\nperson(\"Toby\", \"Hocking\", role = c(\"aut\")),\nperson(\"Guillem\", \"Rigaill\", role = c(\"aut\")),\nperson(\"Daniel\", \"Grose\", role = c(\"aut\")),\nperson(\"Gaetano\", \"Romano\", role = c(\"aut\")),\nperson(\"Fatemeh\", \"Afghah\", role = c(\"aut\")),\nperson(\"Paul\", \"Fearnhead\", role = c(\"aut\")),\nperson(\"Michel\", \"Koskas\", role = c(\"ctb\")),\nperson(\"Arnaud\", \"Liehrmann\", role = c(\"ctb\")))", "Maintainer": "Vincent Runge ", "Description": "Penalized parametric change-point detection by functional\npruning dynamic programming algorithm. The successive means are\nconstrained using a graph structure with edges defining the\nnature of the changes These changes can be unconstrained (type\nstd), up or down constrained (type up and down) or constrained\nby a minimal size jump (type abs). The type null means that the\ngraph allows us to stay on the same segment. To each edge we\ncan associate some additional properties: a minimal gap size, a\npenalty, some robust parameters (K,a) for biweight (K) and\nHuber losses (K and a). The user can also constrain the\ninferred means to lie between some minimal and maximal values.\nData is modeled by a cost with possible use of a robust loss,\nbiweight and Huber (see edge parameters K and a). These costs\nshould have a quadratic, log-linear or a log-log\nrepresentation. This includes quadratic Gaussian cost (type =\n'mean'), log-linear cost (type = 'variance', 'poisson' or\n'exp') and log-log cost (type = 'negbin'). 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