NetDA: an R package for network-based discriminant analysis subject to multilabel classes
type: Item
instance of: scholarly article
title: NetDA: an R package for network-based discriminant analysis subject to multilabel classes
zbMATH Open document ID: 1505.62002
DOI: 10.1155/2022/1041752
author: Li-Pang Chen
published in: Journal of Probability and Statistics
publication date: 2022-11-15T00:00:00Z
review text: Summary: In this paper, we introduce the R package NetDA, which aims to deal with multiclassification with network structures in predictors accommodated. To address the natural feature of network structures, we apply Gaussian graphical models to characterize dependence structures of the predictors and directly estimate the precision matrix. After that, the estimated precision matrix is employed to linear discriminant functions and quadratic discriminant functions. The R package NetDA is now available on CRAN, and the demonstration of functions is summarized as a vignette in the online documentation.
Mathematics Subject Classification ID: 62-04
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62H22
zbMATH DE Number: 7616948
describes a project that uses: CRAN
describes a project that uses: penalizedLDA
describes a project that uses: rda
describes a project that uses: ISLR
describes a project that uses: glasso
describes a project that uses: R
describes a project that uses: ElemStatLearn
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2022/1041752
OpenAlex ID: W4297539956
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description: scientific article; zbMATH DE number 7616948