c( person("Lampros", "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre")), person("Alberto", "Gosso", email = "gosso.alberto@gmail.com", role = "aut") )
Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the 'elmNN' package using 'RcppArmadillo' after the 'elmNN' package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, <doi:10.1016/j.neucom.2005.12.126>.
Lampros Mouselimis [aut, cre],
Alberto Gosso [aut]

c( person(given = "Lampros", family = "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0002-8024-1546")), person(given = "Alberto", family = "Gosso", email = "gosso.alberto@gmail.com", role = "aut") )
Lampros Mouselimis [aut, cre] (<https://orcid.org/0000-0002-8024-1546>),
Alberto Gosso [aut]

c( person(given = "Lampros", family = "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0002-8024-1546")), person(given = "Alberto", family = "Gosso", email = "gosso.alberto@gmail.com", role = "aut"), person(given = "Edwin", family = "de Jonge", email = "edwindjonge@gmail.com", role = "ctb", comment = c(ORCID = "https://orcid.org/0000-0002-6580-4718", Github = "Github Contributor")) )
Lampros Mouselimis [aut, cre] (<https://orcid.org/0000-0002-8024-1546>),
Alberto Gosso [aut],
Edwin de Jonge [ctb] (<https://orcid.org/0000-0002-6580-4718>, Github
Contributor)

c(
person("Ethan", "Heinzen", email = "heinzen.ethan@mayo.edu", role = c("aut", "cre"))
)
A flexible framework for calculating Elo ratings and resulting
rankings of any two-team-per-matchup system (chess, sports leagues, 'Go',
etc.). This implementation is capable of evaluating a variety of matchups,
Elo rating updates, and win probabilities, all based on the basic Elo
rating system.