Overview | Select default execution resources | Basic workflow | Bioconductor objects | Migration from KODAMA 2.4 | Innovations | Renamed or deprecated interfaces | Session information
This package is considered a duplicate. The official version of this package is found at:https://tkcaccia.r-universe.dev/KODAMA
Package: KODAMA 0.99.7
Stefano Cacciatore
KODAMA: Knowledge Discovery by Accuracy Maximization
Knowledge Discovery by Accuracy Maximization using KNN or SIMPLS followed by latent-space LDA. The portable source package vendors the MIT-licensed CPU implementation from the standalone 'kodama-cpp' library. Optional developer builds can link the same public interface to NVIDIA CUDA or Apple Metal backends. Reusable nearest-neighbor graphs, graph-aware visualization, preprocessing, and adapters for common single-cell containers are provided.
Authors:
KODAMA_0.99.7.tar.gz
KODAMA_0.99.7.zip(r-4.7-x86_64)KODAMA_0.99.7.zip(r-4.7-arm64)KODAMA_0.99.7.zip(r-4.6-x86_64)KODAMA_0.99.7.zip(r-4.6-arm64)KODAMA_0.99.7.zip(r-4.5-x86_64)
KODAMA_0.99.7.tgz(r-4.6-x86_64)KODAMA_0.99.7.tgz(r-4.6-arm64)KODAMA_0.99.7.tgz(r-4.5-x86_64)KODAMA_0.99.7.tgz(r-4.5-arm64)
KODAMA_0.99.7.tar.gz(r-4.7-arm64)KODAMA_0.99.7.tar.gz(r-4.7-x86_64)KODAMA_0.99.7.tar.gz(r-4.6-arm64)KODAMA_0.99.7.tar.gz(r-4.6-x86_64)
KODAMA_0.99.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
KODAMA/json (API)
| # Install 'KODAMA' in R: |
| install.packages('KODAMA', repos = c('https://biocstaging.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tkcaccia/kodama/issues
Datasets:
softwareclassificationclusteringdimensionreductiongpupreprocessingvisualizationsinglecellspatialcpp
Last updated from:f2934386d4. Checks:6 ERROR, 10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | ERROR | 232 | ||
| linux-devel-arm64 | OK | 233 | ||
| linux-devel-x86_64 | OK | 238 | ||
| source / vignettes | OK | 303 | ||
| linux-release-arm64 | OK | 212 | ||
| linux-release-x86_64 | OK | 245 | ||
| macos-release-arm64 | OK | 158 | ||
| macos-release-x86_64 | OK | 384 | ||
| macos-oldrel-arm64 | OK | 182 | ||
| macos-oldrel-x86_64 | OK | 345 | ||
| windows-devel-arm64 | ERROR | 295 | ||
| windows-devel-x86_64 | ERROR | 250 | ||
| windows-release-arm64 | ERROR | 283 | ||
| windows-release-x86_64 | ERROR | 197 | ||
| windows-oldrel-x86_64 | ERROR | 213 | ||
| wasm-release | OK | 252 |
Exports:CoreKNNCorePLSLDAdinisurfacehelicoidKNNCVKODAMA_backendkodama_diagnosticskodama_matrixkodama_matrix_graphkodama_pcakodama_timingKODAMA.clusteringKODAMA.diagnosticsKODAMA.graphKODAMA.graph.materializeKODAMA.makeSNNGraphKODAMA.matrixKODAMA.matrix.graphKODAMA.pcaKODAMA.spatial.featuresKODAMA.timingKODAMA.visualizationmakeSNNGraphnormalizationpassing.messagePLSLDACVRunFastPCARunKODAMAclusteringRunKODAMAgraphRunKODAMAmatrixRunKODAMAvisualizationRunSpatialFeatureSelectionscalingspatial_feature_selectionSpatialFeatureSelectionspiralsswissroll
Dependencies:BiobaseBiocGenericsfastEmbedRgenericsRcppS4Vectorswithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| KODAMA core optimization with KNN classifier | CoreKNN |
| KODAMA core optimization with PLS-LDA classifier | CorePLSLDA |
| Generate Dini's surface | dinisurface |
| Generate a helicoid surface | helicoid |
| Cross-validated KNN classification | KNNCV |
| Configure the default KODAMA execution backend | KODAMA_backend |
| Run KODAMA matrix optimization | KODAMA.matrix kodama_matrix |
| KODAMA matrix from a precomputed KNN graph | KODAMA.matrix.graph kodama_matrix_graph |
| Backend-native float32 PCA | KODAMA.pca kodama_pca |
| Cluster a graph or embedding with fastEmbedR | KODAMA.clustering |
| Diagnose wrapper runtime libraries and environment | KODAMA.diagnostics kodama_diagnostics |
| Build the reusable KODAMA graph and visualization initialization | KODAMA.graph KODAMA.makeSNNGraph makeSNNGraph |
| Materialize a handle-backed KODAMA graph | KODAMA.graph.materialize |
| Return a compact timing table for a KODAMA result | KODAMA.timing kodama_timing |
| Visualize a matrix or KODAMA graph with UMAP or openTSNE | KODAMA.visualization |
| Lymphoma gene-expression data | lymphoma |
| Nuclear Magnetic Resonance spectra of urine samples | MetRef |
| Normalize samples | normalization |
| Spatial message-passing preprocessing | passing.message |
| Cross-validated SIMPLS + PLS-LDA classification | PLSLDACV |
| Fast native PCA for matrices and single-cell containers | RunFastPCA RunFastPCA.default RunFastPCA.giotto RunFastPCA.Seurat RunFastPCA.SingleCellExperiment RunFastPCA.SpatialExperiment |
| Cluster KODAMA reductions in matrices and single-cell containers | RunKODAMAclustering RunKODAMAclustering.default RunKODAMAclustering.giotto RunKODAMAclustering.Seurat RunKODAMAclustering.SingleCellExperiment RunKODAMAclustering.SpatialExperiment |
| Run KODAMA graph construction on matrices and single-cell containers | RunKODAMAgraph RunKODAMAgraph.default RunKODAMAgraph.giotto RunKODAMAgraph.Seurat RunKODAMAgraph.SingleCellExperiment RunKODAMAgraph.SpatialExperiment |
| Run KODAMA optimization on matrices and single-cell containers | RunKODAMAmatrix RunKODAMAmatrix.default RunKODAMAmatrix.giotto RunKODAMAmatrix.Seurat RunKODAMAmatrix.SingleCellExperiment RunKODAMAmatrix.SpatialExperiment |
| Visualize KODAMA results stored in single-cell containers | RunKODAMAvisualization RunKODAMAvisualization.default RunKODAMAvisualization.giotto RunKODAMAvisualization.Seurat RunKODAMAvisualization.SingleCellExperiment RunKODAMAvisualization.SpatialExperiment |
| Spatial feature selection for matrices and spatial containers | RunSpatialFeatureSelection RunSpatialFeatureSelection.default RunSpatialFeatureSelection.giotto RunSpatialFeatureSelection.Seurat RunSpatialFeatureSelection.SpatialExperiment SpatialFeatureSelection |
| Scale variables | scaling |
| Fast multi-slide spatial feature screening | KODAMA.spatial.features spatial_feature_selection |
| Generate spiral clusters | spirals |
| Generate a Swiss roll | swissroll |
| State of the Union data | USA |
