SpatPCA: Regularized Principal Component Analysis for Spatial Data

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigenfunctions 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 Jan. 31, 2021, 5:05 p.m.