Package: KODAMA 3.3

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.

Authors:Stefano Cacciatore [aut, trl, cre], Leonardo Tenori [aut]

KODAMA_3.3.tar.gz
KODAMA_3.3.zip(r-4.7)KODAMA_3.3.zip(r-4.6)KODAMA_3.3.zip(r-4.5)
KODAMA_3.3.tgz(r-4.6-x86_64)KODAMA_3.3.tgz(r-4.6-arm64)KODAMA_3.3.tgz(r-4.5-x86_64)KODAMA_3.3.tgz(r-4.5-arm64)
KODAMA_3.3.tar.gz(r-4.7-arm64)KODAMA_3.3.tar.gz(r-4.7-x86_64)KODAMA_3.3.tar.gz(r-4.6-arm64)KODAMA_3.3.tar.gz(r-4.6-x86_64)
KODAMA_3.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
KODAMA/json (API)

# Install 'KODAMA' in R:
install.packages('KODAMA', repos = c('https://tkcaccia.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tkcaccia/kodama/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • lymphoma - Lymphoma Gene Expression Dataset
  • MetRef - Nuclear Magnetic Resonance Spectra of Urine Samples
  • USA - State of the Union Data Set

On CRAN:

Conda:

openblascpp

6.08 score 1 stars 86 scripts 335 downloads 7 mentions 17 exports 21 dependencies

Last updated from:d2dbe30ee6. Checks:11 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING171
linux-devel-x86_64WARNING171
source / vignettesOK227
linux-release-arm64WARNING156
linux-release-x86_64WARNING176
macos-release-arm64WARNING201
macos-release-x86_64WARNING240
macos-oldrel-arm64WARNING246
macos-oldrel-x86_64WARNING333
windows-develWARNING156
windows-releaseWARNING143
windows-oldrelWARNING137
wasm-releaseOK124

Exports:config.tsne.defaultconfig.umap.defaultcore_cppdinisurfacefloydhelicoidkabschKODAMA.matrixKODAMA.visualizationmcplotMDS.defaultsnormalizationpcascalingspiralsswissrolltransformy

Dependencies:askpassherejsonlitelatticeMatrixopensslpngrappdirsRcppRcppArmadilloRcppEigenRcppTOMLreticulaterlangRnanoflannrprojrootRSpectraRtsnesysumapwithr

Knowledge Discovery by Accuracy Maximization

Stefano Cacciatore, Leonardo Tenori

Rendered fromKODAMA.Rmdusingknitr::rmarkdownon May 17 2026.

Last update: 2025-06-03
Started: 2022-06-27