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NetDA: An R Package for Network-Based Discriminant Analysis Subject to Multilabel Classes

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  • Li-Pang Chen
  • Hyungjun Cho

Abstract

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.

Suggested Citation

  • Li-Pang Chen & Hyungjun Cho, 2022. "NetDA: An R Package for Network-Based Discriminant Analysis Subject to Multilabel Classes," Journal of Probability and Statistics, Hindawi, vol. 2022, pages 1-14, September.
    • Handle: RePEc:hin:jnljps:1041752
    DOI: 10.1155/2022/1041752
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