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Rdimtools Development Repository

Rdimtools is an R package for Dimension Reduction (also known as Manifold Learning) and intrinsic Dimension Estimation methods. The philosophy is simple: the more we have at hands, the better we can play.

Installation

You can install a release version from CRAN:

install.packages("Rdimtools")

or the development version from github:

## install.packages("devtools")
devtools::install_github("kisungyou/Rdimtools")

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Version

Install

install.packages('Rdimtools')

Monthly Downloads

10,252

Version

0.4.0

License

GPL (>= 3)

Issues

5

Pull Requests

0

Stars

55

Forks

10

Maintainer

Kisung You

Last Published

September 28th, 2018

Functions in Rdimtools (0.4.0)

est.boxcount

Box-counting Dimension
est.Ustat

ID Estimation with Convergence Rate of U-statistic on Manifold
do.cca

Canonical Correlation Analysis
est.made

Manifold-Adaptive Dimension Estimation
do.dspp

Discriminative Sparsity Preserving Projection
do.kmvp

Kernel-Weighted Maximum Variance Projection
do.dne

Discriminant Neighborhood Embedding
do.isoproj

Isometric Projection
est.nearneighbor1

Intrinsic Dimension Estimation with Near-Neighbor Information
do.cnpe

Complete Neighborhood Preserving Embedding
do.lmds

Landmark Multidimensional Scaling
est.mle2

Maximum Likelihood Esimation with Poisson Process and Bias Correction
est.incisingball

Intrinsic Dimension Estimation with Incising Ball
est.correlation

Correlation Dimension
est.mle1

Maximum Likelihood Esimation with Poisson Process
do.lpca

Locally Principal Component Analysis
est.packing

Intrinsic Dimension Estimation using Packing Numbers
do.lqmi

Linear Quadratic Mutual Information
do.enet

Elastic Net Regularization
est.clustering

Intrinsic Dimension Estimation via Clustering
est.pcathr

PCA Thresholding with Accumulated Variance
do.ammc

Adaptive Maximum Margin Criterion
do.anmm

Average Neighborhood Margin Maximization
do.crp

Collaborative Representation-based Projection
do.adr

Adaptive Dimension Reduction
do.eslpp

Extended Supervised Locality Preserving Projection
do.fscore

Fisher Score
aux.gensamples

Generate model-based samples
est.nearneighbor2

Near-Neighbor Information with Bias Correction
do.elde

Exponential Local Discriminant Embedding
do.asi

Adaptive Subspace Iteration
do.dagdne

Double-Adjacency Graphs-based Discriminant Neighborhood Embedding
do.lde

Local Discriminant Embedding
do.ica

Independent Component Analysis
do.lpe

Locality Pursuit Embedding
do.bpca

Bayesian Principal Component Analysis
do.ldp

Locally Discriminating Projection
do.lscore

Laplacian Score
do.lda

Linear Discriminant Analysis
do.npca

Nonnegative Principal Component Analysis
est.twonn

Intrinsic Dimension Estimation by a Minimal Neighborhood Information
do.npe

Neighborhood Preserving Embedding
do.extlpp

Extended Locality Preserving Projection
do.elpp2

Enhanced Locality Preserving Projection (2013)
do.ppca

Probabilistic Principal Component Analysis
do.lpmip

Locality-Preserved Maximum Information Projection
do.lpfda

Locality Preserving Fisher Discriminant Analysis
do.lpp

Locality Preserving Projection
do.mfa

Marginal Fisher Analysis
do.llp

Local Learning Projections
do.fa

Exploratory Factor Analysis
do.mlie

Maximal Local Interclass Embedding
do.kudp

Kernel-Weighted Unsupervised Discriminant Projection
do.lltsa

Linear Local Tangent Space Alignment
do.mmc

Maximum Margin Criterion
do.ldakm

Combination of LDA and K-means
do.odp

Orthogonal Discriminant Projection
do.rndproj

Random Projection
do.mds

(Classical) Multidimensional Scaling
do.mcfs

Multi-Cluster Feature Selection
do.mmsd

Multiple Maximum Scatter Difference
do.save

Sliced Average Variance Estimation
do.lasso

Least Absolute Shrinkage and Selection Operator
do.modp

Modified Orthogonal Discriminant Projection
do.olda

Orthogonal Linear Discriminant Analysis
do.sda

Semi-Supervised Discriminant Analysis
do.ulda

Uncorrelated Linear Discriminant Analysis
do.lsir

Localized Sliced Inverse Regression
do.mmp

Maximum Margin Projection
do.sdlpp

Sample-Dependent Locality Preserving Projection
do.pca

Principal Component Analysis
do.opls

Orthogonal Partial Least Squares
do.olpp

Orthogonal Locality Preserving Projection
do.lspp

Local Similarity Preserving Projection
do.sir

Sliced Inverse Regression
do.onpp

Orthogonal Neighborhood Preserving Projections
do.crda

Curvilinear Distance Analysis
do.dve

Distinguishing Variance Embedding
do.dm

Diffusion Maps
do.rsir

Regularized Sliced Inverse Regression
do.iltsa

Improved Local Tangent Space Alignment
do.fastmap

FastMap
do.nolpp

Nonnegative Orthogonal Locality Preserving Projection
do.sammc

Semi-Supervised Adaptive Maximum Margin Criterion
do.lea

Locally Linear Embedded Eigenspace Analysis
do.nonpp

Nonnegative Orthogonal Neighborhood Preserving Projections
do.lfda

Local Fisher Discriminant Analysis
do.ispe

Isometric Stochastic Proximity Embedding
do.lsda

Locality Sensitive Discriminant Analysis
do.lsdf

Locality Sensitive Discriminant Feature
do.mvp

Maximum Variance Projection
do.msd

Maximum Scatter Difference
do.pls

Partial Least Squares
do.pflpp

Parameter-Free Locality Preserving Projection
do.keca

Kernel Entropy Component Analysis
do.spca

Sparse Principal Component Analysis
do.ssldp

Semi-Supervised Locally Discriminant Projection
do.slpe

Supervised Locality Pursuit Embedding
do.lamp

Local Affine Multidimensional Projection
do.slpp

Supervised Locality Preserving Projection
do.spp

Sparsity Preserving Projection
do.rlda

Regularized Linear Discriminant Analysis
do.klde

Kernel Local Discriminant Embedding
do.lapeig

Laplacian Eigenmaps
do.udp

Unsupervised Discriminant Projection
do.sammon

Sammon Mapping
do.klfda

Kernel Local Fisher Discriminant Analysis
do.cisomap

Conformal Isometric Feature Mapping
oos.linear

Out-Of-Sample Prediction for Linear Methods
do.kmmc

Kernel Maximum Margin Criterion
do.isomap

Isometric Feature Mapping
oos.linproj

OOS : Linear Projection
do.sne

Stochastic Neighbor Embedding
do.kpca

Kernel Principal Component Analysis
do.mvu

Maximum Variance Unfolding / Semidefinite Embedding
do.crca

Curvilinear Component Analysis
do.lisomap

Landmark Isometric Feature Mapping
do.plp

Piecewise Laplacian-based Projection (PLP)
do.ltsa

Local Tangent Space Alignment
do.lle

Locally Linear Embedding
do.mve

Minimum Volume Embedding
do.splapeig

Supervised Laplacian Eigenmaps
do.spe

Stochastic Proximity Embedding
do.spmds

Spectral Multidimensional Scaling
do.klsda

Kernel Locality Sensitive Discriminant Analysis
do.tsne

t-distributed Stochastic Neighbor Embedding
do.kmfa

Kernel Marginal Fisher Analysis
do.kqmi

Kernel Quadratic Mutual Information
do.ksda

Kernel Semi-Supervised Discriminant Analysis
do.ree

Robust Euclidean Embedding
do.rpca

Robust Principal Component Analysis
aux.preprocess

Preprocessing the data
aux.graphnbd

Construct Nearest-Neighborhood Graph
do.llle

Local Linear Laplacian Eigenmaps
aux.kernelcov

Build a centered kernel matrix K
do.idmap

Interactive Document Map
Rdimtools

Dimension Reduction and Estimation Methods
do.nnp

Nearest Neighbor Projection
aux.shortestpath

Find shortest path using Floyd-Warshall algorithm
aux.pkgstat

Show the number of functions for Rdimtools.