KODAMA: Knowledge Discovery by Accuracy Maximization

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. Based on Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017) Bioinformatics <doi:10.1093/bioinformatics/btw705> and Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>.

Version: 1.6
Depends: R (≥ 2.10.0), stats
Imports: Rcpp (≥ 0.12.4)
LinkingTo: Rcpp, RcppArmadillo
Suggests: rgl, knitr, rmarkdown
Published: 2021-04-18
Author: Stefano Cacciatore ORCID iD [aut, trl, cre], Leonardo Tenori ORCID iD [aut]
Maintainer: Stefano Cacciatore <tkcaccia at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: KODAMA results

Downloads:

Reference manual: KODAMA.pdf
Vignettes: Knowledge Discovery by Accuracy Maximization
Package source: KODAMA_1.6.tar.gz
Windows binaries: r-devel: KODAMA_1.6.zip, r-release: KODAMA_1.6.zip, r-oldrel: KODAMA_1.6.zip
macOS binaries: r-release: KODAMA_1.6.tgz, r-oldrel: KODAMA_1.6.tgz
Old sources: KODAMA archive

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