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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: |
2.4.1 |
| Depends: |
R (≥ 2.10.0), stats, minerva, Rtsne, umap |
| Imports: |
Rcpp (≥ 0.12.4) |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
rgl, knitr, rmarkdown |
| Published: |
2024-11-05 |
| DOI: |
10.32614/CRAN.package.KODAMA |
| Author: |
Stefano Cacciatore
 [aut, trl,
cre],
Leonardo Tenori
[aut] |
| Maintainer: |
Stefano Cacciatore <tkcaccia at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| CRAN checks: |
KODAMA results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=KODAMA
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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