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✨
card.svg |card.png
Rdimtools/json (API)
NEWS

# 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:

Conda:

dimension-estimationdimension-reductionmanifold-learningsubspace-learningopenblascppopenmp

8.80 score 56 stars 8 packages 226 scripts 1.0k downloads 1 mentions 169 exports 40 dependencies

Last updated from:09f56cab12. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK284
linux-devel-x86_64OK268
source / vignettesOK413
linux-release-arm64OK271
linux-release-x86_64OK269
macos-release-arm64OK206
macos-release-x86_64OK395
macos-oldrel-arm64OK193
macos-oldrel-x86_64OK406
windows-develOK302
windows-releaseOK286
windows-oldrelOK259
wasm-releaseOK211

Exports:aux.gensamplesaux.graphnbdaux.kernelcovaux.pkgstataux.preprocessaux.shortestpathdo.adrdo.ammcdo.anmmdo.asido.bmdsdo.bpcado.ccado.cgedo.cisomapdo.cnpedo.crcado.crdado.crpdo.cscoredo.cscoregdo.dagdnedo.disrdo.dmdo.dnedo.dppcado.dsppdo.dvedo.eldedo.elpp2do.enetdo.eslppdo.extlppdo.fado.fastmapdo.fosmoddo.fscoredo.fssemdo.hydrado.icado.idmapdo.iltsado.isomapdo.isoprojdo.ispedo.kecado.kldedo.klfdado.klsdado.kmfado.kmmcdo.kmvpdo.kpcado.kqmido.ksdado.kudpdo.lampdo.lapeigdo.lassodo.ldado.ldakmdo.ldedo.ldpdo.leado.lfdado.lisomapdo.lledo.llledo.llpdo.lltsado.lmdsdo.lpca2006do.lpedo.lpfdado.lpmipdo.lppdo.lqmido.lscoredo.lsdado.lsdfdo.lsirdo.lslsdo.lspedo.lsppdo.ltsado.mcfsdo.mdsdo.mfado.mifsdo.mliedo.mmcdo.mmdsdo.mmpdo.mmsddo.modpdo.msddo.mvedo.mvpdo.mvudo.nnpdo.nolppdo.nonppdo.npcado.npedo.nrsrdo.odpdo.oldado.olppdo.onppdo.oplsdo.pcado.pfado.pflppdo.phatedo.plpdo.plsdo.ppcado.procrustesdo.reedo.rldado.rndprojdo.rpcado.rpcagdo.rsirdo.rsrdo.sammcdo.sammondo.savedo.sdado.sdlppdo.sirdo.slpedo.slppdo.snedo.spcdo.spcado.spedo.specsdo.specudo.splapeigdo.spmdsdo.sppdo.spufsdo.ssldpdo.tsnedo.udfsdo.udpdo.ugfsdo.uldado.uwdfsdo.wdfsest.boxcountest.clusteringest.correlationest.dancoest.gdistnnest.incisingballest.madeest.mindklest.mindmlest.mle1est.mle2est.nearneighbor1est.nearneighbor2est.packingest.pcathrest.twonnest.Ustatoos.linproj

Dependencies:ADMMbackportscheckmateclarabelcliclustercodetoolsCVXRdbscandoParallelfastclusterforeachgenericsgmpgsignalhighsiteratorslabdsvlatticemaotaiMASSMatrixmclustcompmgcvnlmeosqppracmaRANNrbibutilsRcppRcppArmadilloRcppDERcppDistRcppEigenRdpackRSpectraRtsneS7scsslam

Quick Start with Rdimtools Package

Kisung You

Rendered fromquick-start.Rmdusingknitr::rmarkdownon May 19 2026.

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

Readme and manuals

Help Manual

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