1 Introduction | 2 Installation | 2.1 Installation via CRAN | 2.2 Manual installation from source | 2.3 Compatibility issues | 3 Getting Started | 4 Datasets | 4.1 Swiss Roll | 4.2 Ulisse Dini's surface | 4.3 Helicoid | 4.4 Spirals | 4.5 Lymphoma | 4.6 MetRef | 4.7 State of the Union | 4.8 Iris | 5 Starting with KODAMA | 5.2 Evaluation of the Monte Carlo iterative process | 5.1 Adjusting Input Preferences | 5.3 Unsupervised and semi-supervised | 6 Special Notes for Users Upgrading from Previous Version | 7 Errors and Solutions | 7.1 Rcpp, RcppArmadillo and OS X Mavericks "-lgfortran" and "-lquadmath" error | 8 How to Cite this Package | 9 References
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:
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✨
DESCRIPTION
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
Datasets:
Last updated from:d2dbe30ee6. Checks:11 WARNING, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | WARNING | 186 | ||
| linux-devel-x86_64 | WARNING | 165 | ||
| source / vignettes | OK | 228 | ||
| linux-release-arm64 | WARNING | 200 | ||
| linux-release-x86_64 | WARNING | 161 | ||
| macos-release-arm64 | WARNING | 162 | ||
| macos-release-x86_64 | WARNING | 372 | ||
| macos-oldrel-arm64 | WARNING | 114 | ||
| macos-oldrel-x86_64 | WARNING | 189 | ||
| windows-devel | WARNING | 146 | ||
| windows-release | WARNING | 138 | ||
| windows-oldrel | WARNING | 159 | ||
| wasm-release | OK | 122 |
Exports:config.tsne.defaultconfig.umap.defaultcore_cppdinisurfacefloydhelicoidkabschKODAMA.matrixKODAMA.visualizationmcplotMDS.defaultsnormalizationpcascalingspiralsswissrolltransformy
Dependencies:askpassherejsonlitelatticeMatrixopensslpngrappdirsRcppRcppArmadilloRcppEigenRcppTOMLreticulaterlangRnanoflannrprojrootRSpectraRtsnesysumapwithr
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 |
