SpatPCA: Regularized Principal Component Analysis for Spatial Data

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.

Package details

AuthorWen-Ting Wang [aut, cre] (<>), Hsin-Cheng Huang [aut] (<>)
MaintainerWen-Ting Wang <>
Package repositoryView on CRAN
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SpatPCA documentation built on Nov. 13, 2023, 5:06 p.m.