Package: Rdimtools 1.1.5

Rdimtools: Dimension Reduction and Estimation Methods

We provide linear and nonlinear dimension reduction techniques. Intrinsic dimension estimation methods for exploratory analysis are also provided. For more details on the package, see the paper by You and Shung (2022) <doi:10.1016/j.simpa.2022.100414>.

Authors:Kisung You [aut, cre], Changhee Suh [ctb], Dennis Shung [ctb]

Rdimtools_1.1.5.tar.gz
Rdimtools_1.1.5.zip(r-4.7)Rdimtools_1.1.5.zip(r-4.6)Rdimtools_1.1.5.zip(r-4.5)
Rdimtools_1.1.5.tgz(r-4.6-x86_64)Rdimtools_1.1.5.tgz(r-4.6-arm64)Rdimtools_1.1.5.tgz(r-4.5-x86_64)Rdimtools_1.1.5.tgz(r-4.5-arm64)
Rdimtools_1.1.5.tar.gz(r-4.7-arm64)Rdimtools_1.1.5.tar.gz(r-4.7-x86_64)Rdimtools_1.1.5.tar.gz(r-4.6-arm64)Rdimtools_1.1.5.tar.gz(r-4.6-x86_64)
Rdimtools_1.1.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
Rdimtools/json (API)

# Install 'Rdimtools' in R:

install.packages('Rdimtools', repos = c('https://kisungyou.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kisungyou/rdimtools/issues

Pkgdown/docs site:https://www.kisungyou.com

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

iris - Load Iris data
usps - Load USPS handwritten digits data

On CRAN:

dimension-estimation dimension-reduction manifold-learning subspace-learning openblas cpp openmp

8.79 score 57 stars 8 packages 216 scripts 1.1k downloads 1 mentions 169 exports 40 dependencies

Last updated from:09f56cab12. Checks:2 FAIL, 11 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	FAIL	529
linux-devel-x86_64	FAIL	553
source / vignettes	OK	477
linux-release-arm64	OK	323
linux-release-x86_64	OK	339
macos-release-arm64	OK	163
macos-release-x86_64	OK	440
macos-oldrel-arm64	OK	152
macos-oldrel-x86_64	OK	377
windows-devel	OK	299
windows-release	OK	298
windows-oldrel	OK	257
wasm-release	OK	320

Exports:aux.gensamples aux.graphnbd aux.kernelcov aux.pkgstat aux.preprocess aux.shortestpath do.adr do.ammc do.anmm do.asi do.bmds do.bpca do.cca do.cge do.cisomap do.cnpe do.crca do.crda do.crp do.cscore do.cscoreg do.dagdne do.disr do.dm do.dne do.dppca do.dspp do.dve do.elde do.elpp2 do.enet do.eslpp do.extlpp do.fa do.fastmap do.fosmod do.fscore do.fssem do.hydra do.ica do.idmap do.iltsa do.isomap do.isoproj do.ispe do.keca do.klde do.klfda do.klsda do.kmfa do.kmmc do.kmvp do.kpca do.kqmi do.ksda do.kudp do.lamp do.lapeig do.lasso do.lda do.ldakm do.lde do.ldp do.lea do.lfda do.lisomap do.lle do.llle do.llp do.lltsa do.lmds do.lpca2006 do.lpe do.lpfda do.lpmip do.lpp do.lqmi do.lscore do.lsda do.lsdf do.lsir do.lsls do.lspe do.lspp do.ltsa do.mcfs do.mds do.mfa do.mifs do.mlie do.mmc do.mmds do.mmp do.mmsd do.modp do.msd do.mve do.mvp do.mvu do.nnp do.nolpp do.nonpp do.npca do.npe do.nrsr do.odp do.olda do.olpp do.onpp do.opls do.pca do.pfa do.pflpp do.phate do.plp do.pls do.ppca do.procrustes do.ree do.rlda do.rndproj do.rpca do.rpcag do.rsir do.rsr do.sammc do.sammon do.save do.sda do.sdlpp do.sir do.slpe do.slpp do.sne do.spc do.spca do.spe do.specs do.specu do.splapeig do.spmds do.spp do.spufs do.ssldp do.tsne do.udfs do.udp do.ugfs do.ulda do.uwdfs do.wdfs est.boxcount est.clustering est.correlation est.danco est.gdistnn est.incisingball est.made est.mindkl est.mindml est.mle1 est.mle2 est.nearneighbor1 est.nearneighbor2 est.packing est.pcathr est.twonn est.Ustat oos.linproj

Dependencies:ADMM backports checkmate clarabel cli cluster codetools CVXR dbscan doParallel fastcluster foreach generics gmp gsignal highs iterators labdsv lattice maotai MASS Matrix mclustcomp mgcv nlme osqp pracma RANN rbibutils Rcpp RcppArmadillo RcppDE RcppDist RcppEigen Rdpack RSpectra Rtsne S7 scs slam

Quick Start with Rdimtools Package

1. Install and Load | 2. Intrinsic Dimension Estimation | 3. Dimension Reduction

Rendered fromquick-start.Rmdusingknitr::rmarkdown

Last update: 2021-06-10
Started: 2020-04-29

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Generate model-based samples	aux.gensamples
Construct Nearest-Neighborhood Graph	aux.graphnbd
Build a centered kernel matrix K	aux.kernelcov
Show the number of functions for 'Rdimtools'.	aux.pkgstat
Preprocessing the data	aux.preprocess
Find shortest path using Floyd-Warshall algorithm	aux.shortestpath
Adaptive Dimension Reduction	do.adr
Adaptive Maximum Margin Criterion	do.ammc
Average Neighborhood Margin Maximization	do.anmm
Adaptive Subspace Iteration	do.asi
Bayesian Multidimensional Scaling	do.bmds
Bayesian Principal Component Analysis	do.bpca
Canonical Correlation Analysis	do.cca
Constrained Graph Embedding	do.cge
Conformal Isometric Feature Mapping	do.cisomap
Complete Neighborhood Preserving Embedding	do.cnpe
Curvilinear Component Analysis	do.crca
Curvilinear Distance Analysis	do.crda
Collaborative Representation-based Projection	do.crp
Constraint Score	do.cscore
Constraint Score using Spectral Graph	do.cscoreg
Double-Adjacency Graphs-based Discriminant Neighborhood Embedding	do.dagdne
Diversity-Induced Self-Representation	do.disr
Diffusion Maps	do.dm
Discriminant Neighborhood Embedding	do.dne
Dual Probabilistic Principal Component Analysis	do.dppca
Discriminative Sparsity Preserving Projection	do.dspp
Distinguishing Variance Embedding	do.dve
Exponential Local Discriminant Embedding	do.elde
Enhanced Locality Preserving Projection (2013)	do.elpp2
Elastic Net Regularization	do.enet
Extended Supervised Locality Preserving Projection	do.eslpp
Extended Locality Preserving Projection	do.extlpp
Exploratory Factor Analysis	do.fa
FastMap	do.fastmap
Forward Orthogonal Search by Maximizing the Overall Dependency	do.fosmod
Fisher Score	do.fscore
Feature Subset Selection using Expectation-Maximization	do.fssem
Hyperbolic Distance Recovery and Approximation	do.hydra
Independent Component Analysis	do.ica
Interactive Document Map	do.idmap
Improved Local Tangent Space Alignment	do.iltsa
Isometric Feature Mapping	do.isomap
Isometric Projection	do.isoproj
Isometric Stochastic Proximity Embedding	do.ispe
Kernel Entropy Component Analysis	do.keca
Kernel Local Discriminant Embedding	do.klde
Kernel Local Fisher Discriminant Analysis	do.klfda
Kernel Locality Sensitive Discriminant Analysis	do.klsda
Kernel Marginal Fisher Analysis	do.kmfa
Kernel Maximum Margin Criterion	do.kmmc
Kernel-Weighted Maximum Variance Projection	do.kmvp
Kernel Principal Component Analysis	do.kpca
Kernel Quadratic Mutual Information	do.kqmi
Kernel Semi-Supervised Discriminant Analysis	do.ksda
Kernel-Weighted Unsupervised Discriminant Projection	do.kudp
Local Affine Multidimensional Projection	do.lamp
Laplacian Eigenmaps	do.lapeig
Least Absolute Shrinkage and Selection Operator	do.lasso
Linear Discriminant Analysis	do.lda
Combination of LDA and K-means	do.ldakm
Local Discriminant Embedding	do.lde
Locally Discriminating Projection	do.ldp
Locally Linear Embedded Eigenspace Analysis	do.lea
Local Fisher Discriminant Analysis	do.lfda
Landmark Isometric Feature Mapping	do.lisomap
Locally Linear Embedding	do.lle
Local Linear Laplacian Eigenmaps	do.llle
Local Learning Projections	do.llp
Linear Local Tangent Space Alignment	do.lltsa
Landmark Multidimensional Scaling	do.lmds
Locally Principal Component Analysis by Yang et al. (2006)	do.lpca2006
Locality Pursuit Embedding	do.lpe
Locality Preserving Fisher Discriminant Analysis	do.lpfda
Locality-Preserved Maximum Information Projection	do.lpmip
Locality Preserving Projection	do.lpp
Linear Quadratic Mutual Information	do.lqmi
Laplacian Score	do.lscore
Locality Sensitive Discriminant Analysis	do.lsda
Locality Sensitive Discriminant Feature	do.lsdf
Localized Sliced Inverse Regression	do.lsir
Locality Sensitive Laplacian Score	do.lsls
Locality and Similarity Preserving Embedding	do.lspe
Local Similarity Preserving Projection	do.lspp
Local Tangent Space Alignment	do.ltsa
Multi-Cluster Feature Selection	do.mcfs
(Classical) Multidimensional Scaling	do.mds
Marginal Fisher Analysis	do.mfa
Mutual Information for Selecting Features	do.mifs
Maximal Local Interclass Embedding	do.mlie
Maximum Margin Criterion	do.mmc
Metric Multidimensional Scaling	do.mmds
Maximum Margin Projection	do.mmp
Multiple Maximum Scatter Difference	do.mmsd
Modified Orthogonal Discriminant Projection	do.modp
Maximum Scatter Difference	do.msd
Minimum Volume Embedding	do.mve
Maximum Variance Projection	do.mvp
Maximum Variance Unfolding / Semidefinite Embedding	do.mvu do.sde
Nearest Neighbor Projection	do.nnp
Nonnegative Orthogonal Locality Preserving Projection	do.nolpp
Nonnegative Orthogonal Neighborhood Preserving Projections	do.nonpp
Nonnegative Principal Component Analysis	do.npca
Neighborhood Preserving Embedding	do.npe
Non-convex Regularized Self-Representation	do.nrsr
Orthogonal Discriminant Projection	do.odp
Orthogonal Linear Discriminant Analysis	do.olda
Orthogonal Locality Preserving Projection	do.olpp
Orthogonal Neighborhood Preserving Projections	do.onpp
Orthogonal Partial Least Squares	do.opls
Principal Component Analysis	do.pca
Principal Feature Analysis	do.pfa
Parameter-Free Locality Preserving Projection	do.pflpp
Potential of Heat Diffusion for Affinity-based Transition Embedding	do.phate
Piecewise Laplacian-based Projection (PLP)	do.plp
Partial Least Squares	do.pls
Probabilistic Principal Component Analysis	do.ppca
Feature Selection using PCA and Procrustes Analysis	do.procrustes
Robust Euclidean Embedding	do.ree
Regularized Linear Discriminant Analysis	do.rlda
Random Projection	do.rndproj
Robust Principal Component Analysis	do.rpca
Robust Principal Component Analysis via Geometric Median	do.rpcag
Regularized Sliced Inverse Regression	do.rsir
Regularized Self-Representation	do.rsr
Semi-Supervised Adaptive Maximum Margin Criterion	do.sammc
Sammon Mapping	do.sammon
Sliced Average Variance Estimation	do.save
Semi-Supervised Discriminant Analysis	do.sda
Sample-Dependent Locality Preserving Projection	do.sdlpp
Sliced Inverse Regression	do.sir
Supervised Locality Pursuit Embedding	do.slpe
Supervised Locality Preserving Projection	do.slpp
Stochastic Neighbor Embedding	do.sne
Supervised Principal Component Analysis	do.spc
Sparse Principal Component Analysis	do.spca
Stochastic Proximity Embedding	do.spe
Supervised Spectral Feature Selection	do.specs
Unsupervised Spectral Feature Selection	do.specu
Supervised Laplacian Eigenmaps	do.splapeig
Spectral Multidimensional Scaling	do.spmds
Sparsity Preserving Projection	do.spp
Structure Preserving Unsupervised Feature Selection	do.spufs
Semi-Supervised Locally Discriminant Projection	do.ssldp
t-distributed Stochastic Neighbor Embedding	do.tsne
Unsupervised Discriminative Features Selection	do.udfs
Unsupervised Discriminant Projection	do.udp
Unsupervised Graph-based Feature Selection	do.ugfs
Uncorrelated Linear Discriminant Analysis	do.ulda
Uncorrelated Worst-Case Discriminative Feature Selection	do.uwdfs
Worst-Case Discriminative Feature Selection	do.wdfs
Box-counting Dimension	est.boxcount
Intrinsic Dimension Estimation via Clustering	est.clustering
Correlation Dimension	est.correlation
Intrinsic Dimensionality Estimation with DANCo	est.danco
Intrinsic Dimension Estimation based on Manifold Assumption and Graph Distance	est.gdistnn
Intrinsic Dimension Estimation with Incising Ball	est.incisingball
Manifold-Adaptive Dimension Estimation	est.made
MiNDkl	est.mindkl
MINDml	est.mindml
Maximum Likelihood Esimation with Poisson Process	est.mle1
Maximum Likelihood Esimation with Poisson Process and Bias Correction	est.mle2
Intrinsic Dimension Estimation with Near-Neighbor Information	est.nearneighbor1
Near-Neighbor Information with Bias Correction	est.nearneighbor2
Intrinsic Dimension Estimation using Packing Numbers	est.packing
PCA Thresholding with Accumulated Variance	est.pcathr
Intrinsic Dimension Estimation by a Minimal Neighborhood Information	est.twonn
ID Estimation with Convergence Rate of U-statistic on Manifold	est.Ustat
Load Iris data	iris
OOS : Linear Projection	oos.linproj
Load USPS handwritten digits data	usps