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.
c(
person(given = "Artem",
family = "Klevtsov",
role = c("aut", "cre"),
email = "a.a.klevtsov@gmail.com",
comment = c(ORCID = "0000-0003-0492-6647")),
person(given = "Philipp",
family = "Upravitelev",
role = "ctb",
email = "upravitelev@gmail.com"),
person("Google Inc.",
role = "cph")
)
Artem Klevtsov [aut, cre] (<https://orcid.org/0000-0003-0492-6647>),
Philipp Upravitelev [ctb],
Google Inc. [cph]

c(
person("Rodrigo", "Collazo", email = "rodrigocollazo@gmail.com",
role = c("aut")),
person("Pier", "Taranti", email = "ptaranti@gmail.com",
role = c("aut", "cre"))
)
Create and learn Chain Event Graph (CEG) models using a Bayesian
framework. It provides us with a Hierarchical Agglomerative algorithm to
search the CEG model space.
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
<doi:10.1214/15-EJS1068>.
graph, grDevices, graphics, methods, stats, utils, Rgraphviz
'category.R' 'ceg.R' 'event_tree.R' 'staged_tree.R'
'ceg_model.R' 'cont_table.R' 'data_documentation.R'
'model_search_algorithm.R'
'exhaustive_model_search_algorithm.R' 'dinamic_programming.R'
'distribution_of_probability.R' 'dirchlet_distribution.R'
'dirchlet_mpnl_distribution.R'
'heuristic_model_search_algorithm.R' 'lib_funtions.R'
'multinomial_distribution.R' 'oahc.R'
'posterior_distribution.R' 'prior_distribution.R'
'stratified_event_tree.R' 'stratified_staged_tree.R'
'stratified_ceg_model.R' 'variable.R'
Rodrigo Collazo [aut],
Pier Taranti [aut, cre]