Title: A visual, intuitive, easy to use software giving access to a new
type of information buried into your microarray data.
Description: Perform statistical tests to unveil genomic clusters,
produces garphical interpretations of the statistical results
in pdf files, perform a Gene Ontology analysis and produces

Authors@R: c(person("Eemeli", "Leppaaho", role = c("aut","cre"),
person("Seppo", "Virtanen", role = c("aut"),
person("Muhammad", "Ammad-ud-din", role = "ctb",
person("Suleiman A", "Khan", role = "ctb",
person("Tommi", "Suvitaival", role = "ctb",
person("Inka", "Saarinen", role = "ctb",
Description: Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling.

Description: Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.

Authors@R: c(person("Eemeli", "Lepp�aho", role = c("aut","cre"),

Title: Tests for General Factorial Designs
Author: Sarah Friedrich, Frank Konietschke, Markus Pauly
Description: Implemented are the Wald-type statistic,
a permuted version thereof as well as the ANOVA-type statistic
for general factorial designs, even with non-normal error terms
and/or heteroscedastic variances, for crossed designs with an
arbitrary number of factors and nested designs with up to three factors.