Imports: data.table, ggplot2, ggpubr, grDevices, nnet, parallel,
progress, stats, stringr, survival, truncnorm, truncreg, utils

Title: Offline 'Google' Fonts for 'Markdown' and 'Shiny'
person("Mario", "Ranftl", role = c("ctb", "cph"), comment = "google-webfonts-helper"))
Description: Download 'Google' fonts and generate CSS to use in 'rmarkdown' documents and
'shiny' applications. Some popular fonts are included and ready to use.
Imports: utils, htmltools, shiny, crul, jsonlite, glue, usethis
Suggests: knitr, rmarkdown, testthat (>= 2.1.0), vcr, covr
Mario Ranftl [ctb, cph] (google-webfonts-helper)

Description: Download 'Google' fonts and generate 'CSS' to use in 'rmarkdown' documents and

Imports: utils, htmltools, shiny, crul, jsonlite, glue, crayon

Title: Graph-Constrained Functional Pruning Optimal Partitioning
person("Vincent", "Runge", email = "vincent.runge@univ-evry.fr", role = c("aut", "cre")),
person("Toby", "Hocking", 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")))
Description: 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.

person("Daniel", "Grose", role = c("aut")),

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.
Suggests: devtools, knitr, data.table, testthat, rmarkdown, ggplot2,