grid, gridExtra, hnp, gdata, doParallel, doRNG, foreach

c(person("Thiago", "de Paula Oliveira", role=c("aut", "cre"), email = "thiago.paula.oliveira@usp.br"), person("Rafael","de Andrade Moral", role="aut"), person( "John","Hinde",role="aut"), person("Silvio", "Sandoval Zocchi",  role=c("aut","ctb")), person("Clarice", "Garcia Borges Demetrio", role=c("aut", "ctb")))
Thiago de Paula Oliveira [aut, cre],
Rafael de Andrade Moral [aut],
John Hinde [aut],
Silvio Sandoval Zocchi [aut, ctb],
Clarice Garcia Borges Demetrio [aut, ctb]

grid, gridExtra, hnp, gdata, parallel, doSNOW, doRNG, foreach

c(person("Thiago", "de Paula Oliveira", role=c("aut", "cre"), email = "thiago.paula.oliveira@alumni.usp.br"), person("Rafael","de Andrade Moral", role="aut"), person("Silvio", "Sandoval Zocchi",  role="ctb"), person("Clarice", "Garcia Borges Demetrio", role="ctb"), person( "John","Hinde",role="aut"))
Thiago de Paula Oliveira <thiago.paula.oliveira@alumni.usp.br>

c(
person("Thiago", "de Paula Oliveira", role=c("aut", "cre"),
email = "thiago.paula.oliveira@alumni.usp.br",
comment = c(ORCID = "0000-0002-4555-2584")),
person("Rafael","de Andrade Moral", role="aut",
comment = c(ORCID = "0000-0002-0875-3563")),
person("Silvio", "Sandoval Zocchi",  role="ctb",
comment = c(ORCID = "0000-0003-2535-908X")),
person("Clarice", "Garcia Borges Demetrio", role="ctb",
comment = c(ORCID = "0000-0002-3609-178X")),
person( "John","Hinde",role="aut",
comment = c(ORCID = "0000-0001-6507-5204"))
)
Thiago de Paula Oliveira [aut, cre]
(<https://orcid.org/0000-0002-4555-2584>),
Rafael de Andrade Moral [aut] (<https://orcid.org/0000-0002-0875-3563>),
Silvio Sandoval Zocchi [ctb] (<https://orcid.org/0000-0003-2535-908X>),
Clarice Garcia Borges Demetrio [ctb]
(<https://orcid.org/0000-0002-3609-178X>),
John Hinde [aut] (<https://orcid.org/0000-0001-6507-5204>)

Functions for learning chain graphs (and as a special
case, Bayesian networks) via the decomposition approach.

Local Discrimination via Latent Class Models

Providing a method for Local Discrimination via Latent Class Models. The approach is described in <https://www.r-project.org/conferences/useR-2009/abstracts/pdf/Bucker.pdf>.