A Monte Carlo permutation method for multiple test correlation
in case-control association study
A Monte Carlo permutation method for multiple test
correlation in case-control association study

A Monte Carlo permutation method for multiple test correlation
in case/control association study
A Monte Carlo permutation method for multiple test
correlation in case/control association study.

A Monte Carlo permutation method for multiple test correlation
A Monte Carlo permutation method for multiple test
correlation.

c(
person("Ben", "Deivide", email = "ben.deivide@ufsj.edu.br", role = c("aut", "cre")),
person("Daniel", "Furtado", email = "danielff@dex.ufla.br", role = "aut"),
person("Diego", "Arthur", email = "digo.arthur@hotmail.com", role = "aut"),
person("Matheus", "Fernando", email = "matheusfers3500@gmail.com", role = "ctb"))
Performs the execution of the main procedures of multiple
comparisons in the literature, Scott-Knott (1974) <http://www.jstor.org/stable/2529204>,
Batista (2016) <http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations
and export to different extensions of its results. An additional
part of the package is the presence of the performance evaluation
of the tests (Type I error per experiment and the power). This will
assist the user in making the decision for the chosen test.
stats, base, utils, graphics, grDevices, SMR, writexl, xtable,
tcltk, foreach, doParallel, parallel
Ben Deivide [aut, cre],
Daniel Furtado [aut],
Diego Arthur [aut],
Matheus Fernando [ctb]

stats, base, utils, graphics, grDevices, SMR, writexl, xtable,
tcltk, tkrplot, foreach, doParallel, parallel

Perform the model confidence set procedure of Hansen et al (2011).

Perform the model confidence set procedure of Hansen et al (2011) <doi:10.3982/ECTA5771>.

Determine the Optimal Number of Clusters
Ahmed N. Albatineh, Meredith L. Wilcox, Bashar Zogheib, Magdalena Niewiadomska-Bugaj
Identifies the optimal number of clusters by calculating the similarity between
two clustering methods at the same number of clusters using the corrected indices of Rand and
Jaccard as described in Albatineh and Niewiadomska-Bugaj (2011). The number of clusters at
which the index attain its maximum more frequently is a candidate for being the optimal
number of clusters.