KODAMA: Knowledge Discovery by Accuracy Maximization
A self-guided, weakly supervised learning algorithm for feature extraction from noisy and
high-dimensional data. It facilitates the identification of patterns that reflect underlying group
structures across all samples in a dataset. The method incorporates a novel strategy to integrate
spatial information, improving the clarity of results in spatially resolved data.
| Version: |
3.3 |
| Depends: |
R (≥ 2.10.0), stats, Rtsne, umap |
| Imports: |
Rcpp (≥ 0.12.4), Rnanoflann, methods, Matrix |
| LinkingTo: |
Rcpp, RcppArmadillo, Rnanoflann, Matrix |
| Suggests: |
rgl, knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-03-17 |
| 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 |
| Materials: |
README |
| CRAN checks: |
KODAMA results |
Documentation:
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