Package: KODAMA
Version: 1.5
Date: 2018-10-18
Author: Stefano Cacciatore, Leonardo Tenori, Claudio Luchinat, Phillip R. Bennett, and David A. MacIntyre
Maintainer: Stefano Cacciatore <tkcaccia@gmail.com>
Title: Knowledge Discovery by Accuracy Maximization
Description: KODAMA algorithm is an unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. The algorithm was published by Cacciatore et al. 2014 <DOI:10.1073/pnas.1220873111>. Addition functions was introduced by Cacciatore et al. 2017 <DOI:10.1093/bioinformatics/btw705> to facilitate the identification of key features associated with the generated output and are easily interpretable for the user. Cross-validated techniques are also included in this package.
Depends: R (>= 2.10.0), stats
Imports: Rcpp (>= 0.12.4)
LinkingTo: Rcpp, RcppArmadillo
Suggests: rgl, knitr, rmarkdown
VignetteBuilder: knitr
SuggestsNote: No suggestions
License: GPL (>= 2)
Packaged: 2018-10-18 13:19:03 UTC; Stefano
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2018-10-18 15:20:07 UTC
Built: R 3.4.4; x86_64-w64-mingw32; 2019-04-25 19:44:06 UTC; windows
Archs: i386, x64
