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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 interpretability of results in spatially resolved data.
| Version: |
3.0 |
| 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 |
| Published: |
2025-06-03 |
| 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:
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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