Package: HiDimDA 0.2-7

HiDimDA: High Dimensional Discriminant Analysis

Performs linear discriminant analysis in high dimensional problems based on reliable covariance estimators for problems with (many) more variables than observations. Includes routines for classifier training, prediction, cross-validation and variable selection.

Authors:Antonio Pedro Duarte Silva [aut, cre]

HiDimDA_0.2-7.tar.gz
HiDimDA_0.2-7.zip(r-4.5)HiDimDA_0.2-7.zip(r-4.4)HiDimDA_0.2-7.zip(r-4.3)
HiDimDA_0.2-7.tgz(r-4.4-x86_64)HiDimDA_0.2-7.tgz(r-4.4-arm64)HiDimDA_0.2-7.tgz(r-4.3-x86_64)HiDimDA_0.2-7.tgz(r-4.3-arm64)
HiDimDA_0.2-7.tar.gz(r-4.5-noble)HiDimDA_0.2-7.tar.gz(r-4.4-noble)
HiDimDA_0.2-7.tgz(r-4.4-emscripten)HiDimDA_0.2-7.tgz(r-4.3-emscripten)
HiDimDA.pdf |HiDimDA.html✨
HiDimDA/json (API)

# Install 'HiDimDA' in R:
install.packages('HiDimDA', repos = c('https://apedrods.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • AlonDS - Alon Colon Cancer Data Set

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

fortranopenblascpp

1.82 score 33 scripts 313 downloads 99 exports 0 dependencies

Last updated 3 months agofrom:1b9276e9d5. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 10 2025
R-4.5-win-x86_64OKJan 10 2025
R-4.5-linux-x86_64OKJan 10 2025
R-4.4-win-x86_64OKJan 10 2025
R-4.4-mac-x86_64OKJan 10 2025
R-4.4-mac-aarch64OKJan 10 2025
R-4.3-win-x86_64OKJan 10 2025
R-4.3-mac-x86_64OKJan 10 2025
R-4.3-mac-aarch64OKJan 10 2025

Exports:-.DMat-.ShrnkMat-.ShrnkMatInv-.SigFq-.SigFqInv*.DMat*.ShrnkMat*.ShrnkMatInv*.SigFq*.SigFqInv/.DMat/.ShrnkMat/.ShrnkMatInv/.SigFq/.SigFqInv+.DMat+.ShrnkMat+.ShrnkMatInv+.SigFq+.SigFqInvas.matrix.DMatas.matrix.ShrnkMatas.matrix.ShrnkMatInvas.matrix.SigFqas.matrix.SigFqInvcoef.canldaRescoef.clldaResCovECovE.RFcanldaCovE.RFclldaCovE.ScanldaCovE.SclldaDACrossValDldaDlda.data.frameDlda.defaultDMatForbSigapForbSigap1FrobSigApFrobSigAp1ICovEICovE.RFcanldaICovE.RFclldaICovE.ScanldaICovE.Sclldais.Dldais.DMatis.Mldais.RFldais.ShrnkMatis.ShrnkMatInvis.SigFqis.SigFqInvis.SldaLeftMultLeftMult.DMatLeftMult.matrixLeftMult.ShrnkMatLeftMult.ShrnkMatInvLeftMult.SigFqLeftMult.SigFqInvMldaMlda.data.frameMlda.defaultMldaInvEpredict.canldaRespredict.clldaResprint.canldaResprint.clldaResprint.DMatprint.ShrnkMatprint.ShrnkMatInvprint.SigFqprint.SigFqInvRFldaRFlda.data.frameRFlda.defaultRightMultRightMult.DMatRightMult.matrixRightMult.ShrnkMatRightMult.ShrnkMatInvRightMult.SigFqRightMult.SigFqInvSelectVShrnkMatShrnkMatInvShrnkSigESigFqSigFqInvSldaSlda.data.frameSlda.defaultsolve.DMatsolve.ShrnkMatsolve.ShrnkMatInvsolve.SigFqsolve.SigFqInv

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
High Dimensional Discriminant AnalysisHiDimDA-package HiDimDA
Alon Colon Cancer Data SetAlonDS
Class object used for storing the results of a canonical high-dimensional linear discriminant analysis.canldaRes coef.canldaRes CovE.canldaRes ICovE.canldaRes predict.canldaRes print.canldaRes
Class object used for storing the results of a high-dimensional linear discriminant analysis routine (with 'ldafun' argument set to "classification").clldaRes coef.clldaRes CovE.clldaRes ICovE.clldaRes predict.clldaRes print.clldaRes
Generic methods for extracting covariance and inverse covariance matrices from objects storing the results of a Linear Discriminant AnalysisCovE CovE.RFcanlda CovE.RFcllda CovE.Scanlda CovE.Scllda ICovE ICovE.RFcanlda ICovE.RFcllda ICovE.Scanlda ICovE.Scllda
Cross Validation for Discriminant Analysis Classification AlgorithmsDACrossVal
Diagonal Linear Discriminant Analysis.Dlda Dlda.data.frame Dlda.default is.Dlda
DMat objects: diagonal matrices*.DMat +.DMat -.DMat /.DMat as.matrix.DMat DMat is.DMat print.DMat
Approximation of Covariance Matrices from q-factor modelsFrobSigAp FrobSigAp1
MatMult: Specialized matrix multiplication of 'DMat', 'ShrnkMat', 'ShrnkMatInv', 'SigFq' and 'SigFqInv' objects.LeftMult LeftMult.DMat LeftMult.matrix LeftMult.ShrnkMat LeftMult.ShrnkMatInv LeftMult.SigFq LeftMult.SigFqInv RightMult RightMult.DMat RightMult.matrix RightMult.ShrnkMat RightMult.ShrnkMatInv RightMult.SigFq RightMult.SigFqInv
Maximum uncertainty Linear Discriminant Analysis.is.Mlda Mlda Mlda.data.frame Mlda.default
Maximum uncertainty Linear Discriminant Analysis inverse matrix estimator.MldaInvE
High-Dimensional Factor-based Linear Discriminant Analysis.is.RFlda RFlda RFlda.data.frame RFlda.default
Variable Selection for High-Dimensional Supervised Classification.SelectV
ShrnkMat objects: shrunken matrix estimates of a covariance*.ShrnkMat +.ShrnkMat -.ShrnkMat /.ShrnkMat as.matrix.ShrnkMat is.ShrnkMat print.ShrnkMat ShrnkMat
ShrnkMatInv objects: precision (inverse of covariance) matrices associated with shrunken estimates of a covariance*.ShrnkMatInv +.ShrnkMatInv -.ShrnkMatInv /.ShrnkMatInv as.matrix.ShrnkMatInv is.ShrnkMatInv print.ShrnkMatInv ShrnkMatInv
Shrunken Covariance Estimate.ShrnkSigE
SigFq objects: covariance matrices associated with a q-factor model*.SigFq +.SigFq -.SigFq /.SigFq as.matrix.SigFq is.SigFq print.SigFq SigFq
SigFqInv objects: precision (inverse of covariance) matrices associated with a q-factor model*.SigFqInv +.SigFqInv -.SigFqInv /.SigFqInv as.matrix.SigFqInv is.SigFqInv print.SigFqInv SigFqInv
Shrunken Linear Discriminant Analysis.is.Slda Slda Slda.data.frame Slda.default
Solve methods for 'DMat', 'ShrnkMat', 'ShrnkMatInv', 'SigFq' and 'SigFqInv' objects.solve.DMat solve.ShrnkMat solve.ShrnkMatInv solve.SigFq solve.SigFqInv