This package is considered a duplicate. The official version of this package is found at:https://tkcaccia.r-universe.dev/KODAMA

Package: KODAMA 3.3

Stefano Cacciatore

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.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://cran.r-universe.dev', 'https://cloud.r-project.org'))

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:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblas cpp

4.79 score 2 stars 89 scripts 429 downloads 7 mentions 17 exports 21 dependencies

Last updated from:66ba26a3fc. Checks:4 WARNING, 2 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	WARNING	161
linux-devel-x86_64	WARNING	162
source / vignettes	OK	243
linux-release-arm64	WARNING	147
linux-release-x86_64	WARNING	161
wasm-release	OK	130

Exports:config.tsne.default config.umap.default core_cpp dinisurface floyd helicoid kabsch KODAMA.matrix KODAMA.visualization mcplot MDS.defaults normalization pca scaling spirals swissroll transformy

Dependencies:askpass here jsonlite lattice Matrix openssl png rappdirs Rcpp RcppArmadillo RcppEigen RcppTOML reticulate rlang Rnanoflann rprojroot RSpectra Rtsne sys umap withr

Knowledge Discovery by Accuracy Maximization

Stefano Cacciatore, Leonardo Tenori

Rendered fromKODAMA.Rmdusingknitr::rmarkdownon Jun 15 2026.

Last update: 2025-06-03
Started: 2016-10-12

Citation

Readme and manuals

Help Manual

Help page	Topics
Default configuration for Rtsne	config.tsne.default
Default configuration for umap	config.umap.default
Maximization of Cross-Validateed Accuracy Methods	core_cpp
Ulisse Dini Data Set Generator	dinisurface
Find Shortest Paths Between All Nodes in a Graph	floyd
Helicoid Data Set Generator	helicoid
Kabsch Algorithm	kabsch
Knowledge Discovery by Accuracy Maximization	KODAMA.matrix
Visualization of KODAMA output	KODAMA.visualization
Lymphoma Gene Expression Dataset	lymphoma
Evaluation of the Monte Carlo accuracy results	mcplot
Default configuration for RMDS	MDS.defaults
Nuclear Magnetic Resonance Spectra of Urine Samples	MetRef
Normalization Methods	normalization
Truncated Principal Components Analysis	pca
Scaling Methods	scaling
Spirals Data Set Generator	spirals
Swiss Roll Data Set Generator	swissroll
Conversion Classification Vector to Matrix	transformy
State of the Union Data Set	USA