c(person("Alexios", "Ghalanos", role = c("aut", "cre"), email = "alexios@4dscape.com"),person("Tobias","Kley",role="ctb",email="tobias.kley@bristol.ac.uk"))
Alexios Ghalanos [aut, cre],
Tobias Kley [ctb]

c(person("Alexios", "Ghalanos", role = c("aut", "cre"), email = "alexios@4dscape.com"), person("Tobias","Kley",role="ctb",email="tobias.kley@bristol.ac.uk"))
Rsolnp, nloptr, ks, numDeriv, spd, xts, zoo, chron,
SkewHyperbolic, Rcpp,graphics, stats, grDevices, utils

c(person("Alexios", "Galanos", role = c("aut", "cre"), email = "alexios@4dscape.com"),person("Tobias","Kley",role="ctb",email="tobias.kley@bristol.ac.uk"))
Rsolnp, ks, numDeriv, spd, xts, zoo, chron, SkewHyperbolic,
Rcpp,graphics, stats, grDevices, utils
Alexios Galanos [aut, cre],
Tobias Kley [ctb]

Learn Text 'Embeddings' with 'Starspace'
c(
person('Jan', 'Wijffels', role = c('aut', 'cre', 'cph'), email = 'jwijffels@bnosac.be', comment = "R wrapper"),
person('BNOSAC', role = 'cph', comment = "R wrapper"),
person('Facebook, Inc.', role = 'cph', comment = "Starspace (BSD licensed)"))
Wraps the 'StarSpace' library <https://github.com/facebookresearch/StarSpace>
allowing users to calculate word, sentence, article, document, webpage, link and entity 'embeddings'.
By using the 'embeddings', you can perform text based multi-label classification,
find similarities between texts and categories, do collaborative-filtering based recommendation
as well as content-based recommendation, find out relations between entities, calculate
graph 'embeddings' as well as perform semi-supervised learning and multi-task learning on plain text.
The techniques are explained in detail in the paper: 'StarSpace: Embed All The Things!' by Wu et al. (2017), available at <arXiv:1709.03856>.
Rcpp (>= 0.11.5), utils, graphics, stats
Jan Wijffels [aut, cre, cph] (R wrapper),
BNOSAC [cph] (R wrapper),
Facebook, Inc. [cph] (Starspace (BSD licensed))

person("Iegor", "Rudnytskyi", email = "iegor.rudnytskyi@gmail.com",
role = c("aut", "cre"))
A (not yet exhaustive) collection of common models of risk
processes in actuarial science, represented as formal S4 classes. Each class
(risk model) has a simulator of its path, and a plotting function. Further,
a Monte-Carlo estimator of a ruin probability for a finite time is
implemented, using a parallel computation. Currently, the package extends
two classical risk models Cramer-Lundberg and Sparre Andersen models by
including capital injections, that are positive jumps (see Breuer L. and
Badescu A.L. (2014) <doi:10.1080/03461238.2011.636969>). The intent of the
package is to provide a user-friendly interface for ruin processes'
simulators, as well as a solid and extensible structure for future
extensions.
'AllClass.R' 'AllGeneric.R' 'methods-CramerLundberg.R'
'methods-CramerLundbergCapitalInjections.R'
'methods-SparreAndersen.R'
'methods-SparreAndersenCapitalInjections.R' 'plot_path.R'
'ruin_probability.R' 'zzz.R'
testthat, actuar (>= 2.3.0), knitr, rmarkdown