High-Quality Visualizations of Large, High-Dimensional Datasets

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