R (>= 3.0), SpecsVerification (>= 0.4), stats, utils

c(person("MeteoSwiss",
role=c("aut", "cph")),
person("Jonas", "Bhend",  role = c("cre"),
email='jonas.bhend@meteoswiss.ch'),
person("Jacopo", "Ripoldi", role="ctb"),
person("Claudia", "Mignani", role="ctb"),
person("Irina", "Mahlstein", role="ctb"),
person("Rebecca", "Hiller", role="ctb"),
person("Christoph", "Spirig", role="ctb"),
person("Mark", "Liniger", role="ctb"),
person("Andreas", "Weigel", role="ctb"),
person("Joaqu\'in", "Bedia Jimenez", role='ctb'),
person("Matteo", "De Felice", role='ctb'),
person("Stefan", "Siegert", role="ctb"))
MeteoSwiss [aut, cph],
Jonas Bhend [cre],
Jacopo Ripoldi [ctb],
Claudia Mignani [ctb],
Irina Mahlstein [ctb],
Rebecca Hiller [ctb],
Christoph Spirig [ctb],
Mark Liniger [ctb],
Andreas Weigel [ctb],
Joaqu'in Bedia Jimenez [ctb],
Matteo De Felice [ctb],
Stefan Siegert [ctb]

c(person("MeteoSwiss",
role=c("aut", "cph")),
person("Jonas", "Bhend",  role = c("cre"),
email='jonas.bhend@meteoswiss.ch'),
person("Jacopo", "Ripoldi", role="ctb"),
person("Claudia", "Mignani", role="ctb"),
person("Irina", "Mahlstein", role="ctb"),
person("Rebecca", "Hiller", role="ctb"),
person("Christoph", "Spirig", role="ctb"),
person("Mark", "Liniger", role="ctb"),
person("Andreas", "Weigel", role="ctb"),
person("Joaqu\'in", "Bedia Jimenez", role='ctb'),
person("Matteo", "De Felice", role='ctb'),
person("Stefan", "Siegert", role="ctb"),
person("Katrin", "Sedlmeier", role='ctb'))
R (>= 3.0), SpecsVerification (>= 0.5), stats, utils
MeteoSwiss [aut, cph],
Jonas Bhend [cre],
Jacopo Ripoldi [ctb],
Claudia Mignani [ctb],
Irina Mahlstein [ctb],
Rebecca Hiller [ctb],
Christoph Spirig [ctb],
Mark Liniger [ctb],
Andreas Weigel [ctb],
Joaqu'in Bedia Jimenez [ctb],
Matteo De Felice [ctb],
Stefan Siegert [ctb],
Katrin Sedlmeier [ctb]

Generate Alluvial Plots with a Single Line of Code
person( "Bjoern", "Koneswarakantha", role = c("aut","cre"), email = "datistics@gmail.com", comment = c(ORCID = " 0000-0003-4585-7799") )
Alluvial plots are similar to sankey diagrams and visualise categorical data
over multiple dimensions as flows. (Rosvall M, Bergstrom CT (2010) Mapping Change in
Large Networks. PLoS ONE 5(1): e8694. <doi:10.1371/journal.pone.0008694>
Their graphical grammar however is a bit more complex then that of a regular x/y
plots. The 'ggalluvial' package made a great job of translating that grammar into
'ggplot2' syntax and gives you many option to tweak the appearance of an alluvial
plot, however there still remains a multi-layered complexity that makes it difficult
to use 'ggalluvial' for explorative data analysis. 'easyalluvial' provides a simple
interface to this package that allows you to produce a decent alluvial plot from any
dataframe in either long or wide format from a single line of code while also handling
continuous data. It is meant to allow a quick visualisation of entire dataframes
with a focus on different colouring options that can make alluvial plots a great
tool for data exploration.
purrr , tidyr , dplyr , forcats , ggalluvial , ggplot2 ,
RColorBrewer , recipes , rlang , stringr , magrittr , tibble
Bjoern Koneswarakantha [aut, cre] (<https://orcid.org/
0000-0003-4585-7799>)