c(person("Oliver", "Schaer", email = "info@oliverschaer.ch",
role = c("aut", "cre")),
person("Nikolaos", "Kourentzes", email = "nikolaos@kourentzes.com",
role = c("aut")))
Various diffusion models to forecast new product growth. Currently
the package contains Bass, Gompertz and Gamma/Shifted Gompertz curves. See
Meade and Islam (2006) <doi:10.1016/j.ijforecast.2006.01.005>.
Oliver Schaer [aut, cre],
Nikolaos Kourentzes [aut]

Joseph Richards <jwrichar@stat.cmu.edu>, and Ann Lee
<annlee@stat.cmu.edu>
Implements diffusion map method of data parametrization,
including creation and visualization of diffusion map and
course-graining using diffusion K-means.

Implements diffusion map method of data parametrization,
including creation and visualization of diffusion map,
clustering with diffusion K-means and regression using adaptive
regression model.
R (>= 2.4.0), scatterplot3d, igraph, Matrix

Implements diffusion map method of data
parametrization, including creation and visualization of
diffusion map, clustering with diffusion K-means and
regression using adaptive regression model.
Orphaned and corrected on 2018-07-21 as check problems
were not corrected despite reminders.

Implements diffusion map method of data
parametrization, including creation and visualization of
diffusion map, clustering with diffusion K-means and
regression using adaptive regression model.
Richards (2009) <doi:10.1088/0004-637X/691/1/32>.
c(
person(
"Joseph", "Richards",
email = "joeyrichar@gmail.com",
role = c("aut"),
comment = c(github = "joeyrichar")
),
person(
"Robrecht", "Cannoodt",
email = "rcannood@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-3641-729X", github = "rcannood")
)
)
scatterplot3d, graphics, igraph, Matrix, stats
Joseph Richards [aut] (joeyrichar),
Robrecht Cannoodt [aut, cre] (<https://orcid.org/0000-0003-3641-729X>,
rcannood)

person("Simon", "Dirmeier", email = "simon.dirmeier@gmx.de", role = c("aut", "cre"))
Implementation of network diffusion algorithms such as insulated
heat propagation or Markov random walks. Network diffusion algorithms generally
spread information in the form of node weights along the edges of a graph to other nodes.
These weights can for example be interpreted as temperature, an initial amount
of water, the activation of neurons in the brain, or the location of a random
surfer in the internet. The information (node weights) is iteratively propagated
to other nodes until a equilibrium state or stop criterion occurs.
knitr, rmarkdown, testthat, lintr, igraph