High-Quality Visualizations of Large, High-Dimensional Datasets


[Up] [Top]

Documentation for package ‘largeVis’ version 0.2.1.1

Help Pages

as.dendrogram.hdbscan as.dendrogram.hdbscan
as.dist.edgematrix Build an nearest-neighbor graph weighted by distance.
buildEdgeMatrix Build an nearest-neighbor graph weighted by distance.
buildWijMatrix buildWijMatrix
buildWijMatrix.CsparseMatrix buildWijMatrix
buildWijMatrix.edgematrix buildWijMatrix
buildWijMatrix.TsparseMatrix buildWijMatrix
distance Calculate pairwise Euclidean or angular distances efficiently
distance.CsparseMatrix Calculate pairwise Euclidean or angular distances efficiently
distance.matrix Calculate pairwise Euclidean or angular distances efficiently
distance.TsparseMatrix Calculate pairwise Euclidean or angular distances efficiently
ggManifoldMap Visualize an embedding by ggplotting with images
gplot gplot
hdbscan hdbscan
largeVis Apply the LargeVis algorithm for visualizing large high-dimensional datasets.
lof Local Outlier Factor Score
lv_dbscan lv_dbscan
lv_optics lv_optics
manifoldMap Visualize an embedding by plotting with images
manifoldMapStretch manifoldMapStretch
neighborsToVectors A utility function to convert a k-NN graph to a pair of 0-indexed vectors of indices.
projectKNNs Project a distance matrix into a lower-dimensional space.
randomProjectionTreeSearch Find approximate k-Nearest Neighbors using random projection tree search.
randomProjectionTreeSearch.CsparseMatrix Find approximate k-Nearest Neighbors using random projection tree search.
randomProjectionTreeSearch.matrix Find approximate k-Nearest Neighbors using random projection tree search.
randomProjectionTreeSearch.TsparseMatrix Find approximate k-Nearest Neighbors using random projection tree search.
sgdBatches sgdBatches