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HiDimDA (version 0.2-6)

High Dimensional Discriminant Analysis

Description

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.

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Version

Install

install.packages('HiDimDA')

Monthly Downloads

240

Version

0.2-6

License

GPL (>= 3)

Maintainer

Pedro Silva

Last Published

February 25th, 2024

Functions in HiDimDA (0.2-6)

Slda

Shrunken Linear Discriminant Analysis.
ShrnkSigE

Shrunken Covariance Estimate.
canldaRes

Class object used for storing the results of a canonical high-dimensional linear discriminant analysis.
SigFq

SigFq objects: covariance matrices associated with a q-factor model
ShrnkMatInv

ShrnkMatInv objects: precision (inverse of covariance) matrices associated with shrunken estimates of a covariance
SelectV

Variable Selection for High-Dimensional Supervised Classification.
SigFqInv

SigFqInv objects: precision (inverse of covariance) matrices associated with a q-factor model
ShrnkMat

ShrnkMat objects: shrunken matrix estimates of a covariance
MldaInvE

Maximum uncertainty Linear Discriminant Analysis inverse matrix estimator.
RFlda

High-Dimensional Factor-based Linear Discriminant Analysis.
clldaRes

Class object used for storing the results of a high-dimensional linear discriminant analysis routine (with ‘ldafun’ argument set to “classification”).
solve

Solve methods for ‘DMat’, ‘ShrnkMat’, ‘ShrnkMatInv’, ‘SigFq’ and ‘SigFqInv’ objects.
FrobSigAp

Approximation of Covariance Matrices from q-factor models
HiDimDA-internal

Internal HiDimDA Functions
MatMult

MatMult: Specialized matrix multiplication of ‘DMat’, ‘ShrnkMat’, ‘ShrnkMatInv’, ‘SigFq’ and ‘SigFqInv’ objects.
AlonDS

Alon Colon Cancer Data Set
HiDimDA-package

High Dimensional Discriminant Analysis
Dlda

Diagonal Linear Discriminant Analysis.
CovE

Generic methods for extracting covariance and inverse covariance matrices from objects storing the results of a Linear Discriminant Analysis
DACrossVal

Cross Validation for Discriminant Analysis Classification Algorithms
DMat

DMat objects: diagonal matrices
Mlda

Maximum uncertainty Linear Discriminant Analysis.