c(person("Oleksii", "Pokotylo", role=c("aut", "cre"),
email = "alexey.pokotylo@gmail.com"),
person("Pavlo", "Mozharovskyi", role=c("aut"),
email = "pavlo.mozharovskyi@telecom-paris.fr"),
person("Rainer", "Dyckerhoff", role=c("aut"),
email = "rainer.dyckerhoff@statistik.uni-koeln.de"),
person("Stanislav", "Nagy", role=c("aut"),
email = "nagy@karlin.mff.cuni.cz"))

stats, utils, graphics, grDevices, MASS, class, robustbase,
sfsmisc, geometry
Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.

c(person("Rich", "FitzJohn", role = c("aut", "cre"),
email = "rich.fitzjohn@gmail.com"),
person("Wes", "Hinsley", role = "aut"),
person("Imperial College of Science, Technology and Medicine",
role = "cph"))
Solves ordinary and delay differential equations, where
the objective function is written in either R or C.  Suitable only
for non-stiff equations, the solver uses a 'Dormand-Prince' method
that allows interpolation of the solution at any point.  This
approach is as described by Hairer, Norsett and Wanner (1993)
<ISBN:3540604529>.  Support is also included for iterating
difference equations.
deSolve, knitr, microbenchmark, rmarkdown, testthat
Rich FitzJohn [aut, cre],
Wes Hinsley [aut],
Imperial College of Science, Technology and Medicine [cph]

person("Niall", "McGearailt", role = c("aut", "cre"),email = "niall@dukeanalytics.com")
A wrapper for the Duke Analytics model deployment API. See <http://www.dukeanalytics.com/products/dukedeployR.php> for more details.

Dynamical Deterministic Effects Propagation Networks: Infer
signalling networks for timecourse RPPA data.
Given a matrix of reverse-phase-protein-array timecourse
data, containing one or more experimental measurements for the
signalling patterns among proteins in cells, the package
attempts to model the time-dependent propagation of active and
passive states depending on a network. The most likely network
structure given the data is reconstructed.