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:
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