KODAMA: Knowledge Discovery by Accuracy Maximization

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.

Version: 1.5
Depends: R (≥ 2.10.0), stats
Imports: Rcpp (≥ 0.12.4)
LinkingTo: Rcpp, RcppArmadillo
Suggests: rgl, knitr, rmarkdown
Published: 2018-10-18
Author: Stefano Cacciatore, Leonardo Tenori, Claudio Luchinat, Phillip R. Bennett, and David A. MacIntyre
Maintainer: Stefano Cacciatore <tkcaccia at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: KODAMA results


Reference manual: KODAMA.pdf
Vignettes: Knowledge Discovery by Accuracy Maximization
Package source: KODAMA_1.5.tar.gz
Windows binaries: r-devel: KODAMA_1.5.zip, r-devel-gcc8: KODAMA_1.5.zip, r-release: KODAMA_1.5.zip, r-oldrel: KODAMA_1.5.zip
OS X binaries: r-release: KODAMA_1.5.tgz, r-oldrel: KODAMA_1.5.tgz
Old sources: KODAMA archive


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