Package: KODAMA 3.1

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_3.1.tar.gz
KODAMA_3.1.zip(r-4.5)KODAMA_3.1.zip(r-4.4)KODAMA_3.1.zip(r-4.3)
KODAMA_3.1.tgz(r-4.4-x86_64)KODAMA_3.1.tgz(r-4.4-arm64)KODAMA_3.1.tgz(r-4.3-x86_64)KODAMA_3.1.tgz(r-4.3-arm64)
KODAMA_3.1.tar.gz(r-4.5-noble)KODAMA_3.1.tar.gz(r-4.4-noble)
KODAMA_3.1.tgz(r-4.4-emscripten)KODAMA_3.1.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:

36 exports 2.84 score 21 dependencies 1 dependents 7 mentions 59 scripts 527 downloads

Last updated 27 days agofrom:269c89189c. Checks:OK: 1 ERROR: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 12 2024
R-4.5-win-x86_64ERRORSep 12 2024
R-4.5-linux-x86_64ERRORSep 12 2024
R-4.4-win-x86_64ERRORSep 12 2024
R-4.4-mac-x86_64ERRORSep 12 2024
R-4.4-mac-aarch64ERRORSep 12 2024
R-4.3-win-x86_64ERRORSep 12 2024
R-4.3-mac-x86_64ERRORSep 12 2024
R-4.3-mac-aarch64ERRORSep 12 2024

Exports:categorical.testcontinuous.testcore_cppcorrelation.testdinisurfacefloydfrequency_matchinghelicoidk.testkabschknn_Armadilloknn.double.cvknn.kodamaKNNPLSDACVKODAMA.matrixKODAMA.visualizationloadsmcplotMDS.defaultsmulti_analysisnormalizationpcapls.double.cvpls.kodamaPLSDACVquality_controlrefine_clusterRQscalingspiralsswissrolltransformytsne.defaultstxtsummaryumap.defaultsvertex

Dependencies:askpassherejsonlitelatticeMatrixminervaopensslpngrappdirsRcppRcppArmadilloRcppEigenRcppTOMLreticulaterlangrprojrootRSpectraRtsnesysumapwithr

Knowledge Discovery by Accuracy Maximization

Stefano Cacciatore, Leonardo Tenori

Rendered fromKODAMA.Rmdusingknitr::rmarkdownon Sep 12 2024.

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