c(person("Simon", "Urbanek", role = c("aut"), email = "simon.urbanek@R-project.org"),
person("Alassane", "Samba", role = c("aut","cre"), email = "alassane.samba@orange.com"),
person("AT&T", role = c("cph")),
person("Tech Mahindra", role = c("cph")),
person("Orange", role = c("cph")))
Create, upload and run 'Acumos' R models.
'Acumos' (<https://www.acumos.org>) is a platform and open source framework intended to make it easy to build,
share, and deploy AI apps. 'Acumos' is part of the 'LF AI Foundation', an umbrella
organization within The Linux Foundation. With this package, user can create a
component, and push it to an 'Acumos' platform.
httr, jsonlite, RProtoBuf, Rserve, RestRserve, yaml
https://www.acumos.org (Acumos project website)
https://gerrit.acumos.org/r/gitweb?p=acumos-r-client.git (code
repository) https://github.com/acumos/acumos-r-client (mirror
repository)
https://docs.acumos.org/en/latest/submodules/acumos-r-client/docs
(docs)
Simon Urbanek [aut],
Alassane Samba [aut, cre],
AT&T [cph],
Tech Mahindra [cph],
Orange [cph]

Create, upload and run 'Acumos' R models.
'Acumos' (<https://www.acumos.org>) is a platform and open source framework intended to make it easy to build,
share, and deploy AI apps. 'Acumos' is part of the 'LF AI Foundation', an umbrella
organization within 'The Linux Foundation'. With this package, user can create a
component, and push it to an 'Acumos' platform.

Performs boosting algorithms for a binary response
Mark Culp, Kjell Johnson, and George Michailidis
Performs discrete, real, and gentle boost under both exponential and
logistic loss on a given data set.  The package ada provides a straightforward,
well-documented, and broad boosting routine for classification, ideally suited
for small to moderate-sized data sets.  Please refer to the Url below for more
information.

ada: an R package for stochastic boosting
Performs discrete, real, and gentle boost under both
exponential and logistic loss on a given data set.  The package
ada provides a straightforward, well-documented, and broad
boosting routine for classification, ideally suited for small
to moderate-sized data sets.  Please refer to the Url below for
more information.