c(
person("Vincent", "Runge", email = "vincent.runge@univ-evry.fr", role = c("aut", "cre")),
person("Toby", "Hocking", role = c("aut")),
person("Guillem", "Rigaill", role = c("aut")),
person("Daniel", "Grose", 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")))
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]

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.
devtools, knitr, data.table, testthat, rmarkdown, ggplot2,
penaltyLearning

c(
person("Jiacheng", "Lou", email = "loujiacheng1986@foxmail.com", role = c("aut", "cre")),
person("Jing", "Zhang", role = "aut"),
person("Yizhu", "Lvy", role = "aut"),
person("Zhi", "Jin", role = "aut")
)
It is not very easy to define segments for y-axis in a 'ggplot2' plot. gg.gap() function in this package can carry it out.
Jiacheng Lou [aut, cre],
Jing Zhang [aut],
Yizhu Lvy [aut],
Zhi Jin [aut]

Mini Bubble Plots for Comparison of Discrete Data with 'ggplot2'
person("Thomas",
"Schwarzl",
email = "schwarzl@embl.de",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-7697-7000"))
When comparing discrete data mini bubble plots allow displaying
more information than traditional bubble plots via colour, shape or labels.
Exact overlapping coordinates will be transformed so they surround the
original point circularly without overlapping. This is implemented as a
position_surround() function for 'ggplot2'.
Thomas Schwarzl [aut, cre] (<https://orcid.org/0000-0001-7697-7000>)