graphics, methods, Rcpp (>= 0.12.10), spatstat, stats
bang, inline, knitr, microbenchmark, revdbayes, rmarkdown,
testthat

Implementation of Unsupervised Neural Architectures
c(person("David", "Charte", email = "fdavidcl@ugr.es", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-4830-9512")), person("Francisco", "Charte",
email = "francisco@fcharte.com", role = "aut", comment = c(ORCID = "0000-0002-3083-8942")),
person("Francisco", "Herrera", email = "herrera@decsai.ugr.es", role = "aut"))
Implementation of several unsupervised neural networks,
from building their architecture to their training and evaluation. Available
networks are auto-encoders including their main variants: sparse, contractive,
denoising, robust and variational, as described in Charte et al. (2018)
<doi:10.1016/j.inffus.2017.12.007>.
knitr, magrittr (>= 1.5), rmarkdown, testthat (>= 2.0.0)
Python (>= 2.7); keras <https://keras.io/> (>= 2.1)
David Charte [aut, cre] (<https://orcid.org/0000-0002-4830-9512>),
Francisco Charte [aut] (<https://orcid.org/0000-0002-3083-8942>),
Francisco Herrera [aut]

A RUT (Rol Unico Tributario) is an unique and personal identification number implemented in Chile to identify citizens and taxpayers. Rutifier allows to validate if a RUT exist or not and change between the different formats a RUT can have.

Detect and Remove Unwanted Variation using Negative Controls
The algorithms in this package attempt to adjust for systematic errors of unknown origin in high-dimensional data.  The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well.  The algorithms included in this package are RUV-2, RUV-4, RUV-inv, and RUV-rinv, along with various supporting algorithms.  These algorithms were proposed by Gagnon-Bartsch and Speed (2012), and by Gagnon-Bartsch, Jacob and Speed (2013).  The algorithms require the user to specifiy a set of negative control variables, as described in the references.