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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