Dimension Reduction and Estimation Methods

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Documentation for package ‘Rdimtools’ version 0.1.3

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Rdimtools-package Dimension Reduction and Estimation Methods
aux.gensamples Generate model-based samples
aux.graphnbd Find nearest neighborhood
aux.kernelcov Build a centered kernel matrix K
aux.pkgstat Show the number of functions for 'Rdimtools'.
aux.preprocess Centering, decorrelating, or whitening of the data
aux.shortestpath Find shortest path using Floyd-Warshall algorithm
do.cca Canonical Correlation Analysis
do.cisomap Conformal Isometric Feature Mapping
do.dm Diffusion Maps
do.fa Exploratory Factor Analysis
do.ica Independent Component Analysis
do.isomap Isometric Feature Mapping
do.keca Kernel Entropy Component Analysis
do.kpca Kernel Principal Component Analysis
do.lapeig Laplacian Eigenmaps
do.lda Linear Discriminant Analysis
do.lisomap Landmark Isometric Feature Mapping
do.lle Locally-Linear Embedding
do.lmds Landmark Multidimensional Scaling
do.lpp Locality Preserving Projections
do.ltsa Local Tangent Space Alignment
do.mds (Classical) Multidimensional Scaling
do.mvu Maximum Variance Unfolding / Semidefinite Embedding
do.npe Neighborhood Preserving Embedding
do.olpp Orthogonal Locality Preserving Projection
do.opls Orthogonal Partial Least Squares
do.pca Principal Component Analysis
do.plp Piecewise Laplacian-based Projection (PLP)
do.pls Partial Least Squares
do.ree Robust Euclidean Embedding
do.rndproj Random Projection
do.sammon Sammon Mapping
do.sde Maximum Variance Unfolding / Semidefinite Embedding
do.sne Stochastic Neighbor Embedding
do.tsne t-distributed Stochastic Neighbor Embedding
est.boxcount Box-counting dimension
est.correlation Correlation Dimension
Rdimtools Dimension Reduction and Estimation Methods