Title: R Interface for the C Implementation of CEC Benchmark Functions
person("Eryk", "Warchulski", role = c("aut", "cre"), email = "ewarchul@gmail.com"),
person("Dariusz", "Jagodziński", role = c("cph"), email = "d.jagodzinski@elka.pw.edu.pl"),
person("Yasser", "Gonzalez-Fernandez", role = c("cph"), email = "yasser@yassergonzalez.com"),
person("Mauricio", "Zambrano-Bigiarini", role = c("cph"), email = "mzb.devel@gmail.com")
Description: Goal of this package is to provide access to benchmark functions defined for
the Special Session and Competition on Real-Parameter Single Objective Optimization in one place.
The package contains functions from following years: 2013, 2014, 2017, 2021 (<https://github.com/P-N-Suganthan>).
Implementations of CEC-2013 (Y. Gonzalez-Fernandez & M. Zambrano-Bigiarini) and CEC2017 (D. Jagodziński) are taken from existed R packages. Also,
the original C source code has been cleaned and reorganized for better readability.
Suggests: testthat (>= 3.0.0), purrr (>= 0.3.4)

The package contains functions from following years: 2013, 2014, 2015,

Description: R bindings of the Google Compact Encoding Detection library
(<https://github.com/google/compact_enc_det>). The library takes as input
a source buffer of raw text bytes and probabilistically determines the
most likely encoding for that text. It was designed with accuracy,
robustness, small size, and speed in mind.

person("Rodrigo", "Collazo", email = "rodrigocollazo@gmail.com",
person("Pier", "Taranti", email = "ptaranti@gmail.com",
Description: Create and learn Chain Event Graph (CEG) models using a Bayesian
framework. It provides us with a Hierarchical Agglomerative algorithm to
The package also includes several facilities for visualisations of the
objects associated with a CEG. The CEG class can represent a range of
relational data types, and supports arbitrary vertex, edge and graph
attributes. A Chain Event Graph is a tree-based graphical model that
provides a powerful graphical interface through which domain experts can
easily translate a process into sequences of observed events using plain
language. CEGs have been a useful class of graphical model especially to
capture context-specific conditional independences. References: Collazo R,
Gorgen C, Smith J. Chain Event Graph. CRC Press, ISBN 9781498729604, 2018
(forthcoming); and Barday LM, Collazo RA, Smith JQ, Thwaites PA, Nicholson AE.
The Dynamic Chain Event Graph. Electronic Journal of Statistics, 9 (2) 2130-2169
Imports: graph, grDevices, graphics, methods, stats, utils, Rgraphviz
Collate: 'category.R' 'ceg.R' 'event_tree.R' 'staged_tree.R'