MASS, inlinedocs, ggplot2 (>= 2.0), lattice, alphahull, nlme,
ElemStatLearn, lars, latticeExtra

An extensible framework
for automatically placing direct labels onto multicolor 'lattice' or
'ggplot2' plots.
Label positions are described using Positioning Methods
which can be re-used across several different plots.
There are heuristics for examining "trellis" and "ggplot" objects
and inferring an appropriate Positioning Method.

MASS, inlinedocs, ggplot2 (>= 2.0), rlang, lattice,
alphahull, nlme, lars, latticeExtra, dplyr
Orphaned and corrected on 2020-01-29 as check problems
were not corrected in time.

MASS, inlinedocs, ggplot2 (>= 2.0), rlang, lattice,
alphahull, nlme, ElemStatLearn, lars, latticeExtra, dplyr

MASS, inlinedocs, ggplot2 (>= 2.0), rlang, lattice,
alphahull, nlme, lars, latticeExtra, testthat

MASS, knitr, markdown, inlinedocs, RColorBrewer, ggplot2 (>=
2.0), rlang, lattice, alphahull, nlme, lars, latticeExtra,
dplyr, ggthemes, testthat

MASS, knitr, markdown, inlinedocs, ggplot2 (>= 2.0), rlang,
lattice, alphahull, nlme, lars, latticeExtra, dplyr, ggthemes,
testthat

Creates an Interactive Tree Structure of a Directory
person("Louis", "Sirugue", email = "lsirugue@protonmail.com", role = c("aut", "cre"))
Represents the content of a directory as an interactive collapsible tree. Offers
the possibility to assign a text (e.g., a 'Readme.txt') to each folder
(represented as a clickable node), so that when the user hovers the pointer
over a node, the corresponding text is displayed as a tooltip.
collapsibleTree (>= 0.1.6), data.tree (>= 0.7.6)

Dirichlet model of consumer buying behavior for marketing research

Build Dirichlet Process Objects for Bayesian Modelling
c(
person("Dean", "Markwick", email="dean.markwick@talk21.com", role=c("aut", "cre")),
person("Gordon", "J. Ross", email="gordon@gordonjross.co.uk", role=c("aut"))
)
Create Dirichlet process objects that can be used as infinite mixture models in a variety of ways.
Some examples include; density estimation, Poisson process intensity inference, hierarchical modelling and clustering.
See Teh, Y. W. (2011) <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, among many other sources.
Dean Markwick [aut, cre],
Gordon J. Ross [aut]