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.
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