Package: KODAMA 3.0

KODAMA: Knowledge Discovery by Accuracy Maximization

An unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. Based on Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017) Bioinformatics <doi:10.1093/bioinformatics/btw705> and Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>.

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

KODAMA_3.0.tar.gz
KODAMA_3.0.zip(r-4.5)KODAMA_3.0.zip(r-4.4)KODAMA_3.0.zip(r-4.3)
KODAMA_3.0.tgz(r-4.4-x86_64)KODAMA_3.0.tgz(r-4.4-arm64)KODAMA_3.0.tgz(r-4.3-x86_64)KODAMA_3.0.tgz(r-4.3-arm64)
KODAMA_3.0.tar.gz(r-4.5-noble)KODAMA_3.0.tar.gz(r-4.4-noble)
KODAMA_3.0.tgz(r-4.4-emscripten)KODAMA_3.0.tgz(r-4.3-emscripten)
KODAMA.pdf |KODAMA.html✨
KODAMA/json (API)

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

Peer review:

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

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

On CRAN:

openblascpp

7.02 score 1 stars 1 packages 63 scripts 802 downloads 7 mentions 30 exports 22 dependencies

Last updated 2 months agofrom:66fb558358. Checks:1 OK, 8 ERROR. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 10 2025
R-4.5-win-x86_64ERRORJan 10 2025
R-4.5-linux-x86_64ERRORJan 10 2025
R-4.4-win-x86_64ERRORJan 10 2025
R-4.4-mac-x86_64ERRORJan 10 2025
R-4.4-mac-aarch64ERRORJan 10 2025
R-4.3-win-x86_64ERRORJan 10 2025
R-4.3-mac-x86_64ERRORJan 10 2025
R-4.3-mac-aarch64ERRORJan 10 2025

Exports:categorical.testcontinuous.testcore_cppcorrelation.testdinisurfacefloydfrequency_matchinghelicoidk.testkabschKODAMA.matrixKODAMA.visualizationloadsmcplotMDS.defaultsmulti_analysisnormalizationpcaPLSDACV_fastplsPLSDACV_simplsquality_controlrefine_clusterscalingspiralsswissrolltransformytsne.defaultstxtsummaryumap.defaultsvertex

Dependencies:askpassherejsonlitelatticeMatrixminervaopensslpngrappdirsRcppRcppArmadilloRcppEigenRcppTOMLreticulaterlangRnanoflannrprojrootRSpectraRtsnesysumapwithr

Knowledge Discovery by Accuracy Maximization

Stefano Cacciatore, Leonardo Tenori

Rendered fromKODAMA.Rmdusingknitr::rmarkdownon Jan 10 2025.

Last update: 2023-06-06
Started: 2022-06-27