SpatMix: Spatial Mixture Models for Clustering
Fits spatial mixture models, including spatial Gaussian mixtures
and mixtures of spatial factor analyzers, to complete or
incomplete data. Spatial decay can be represented by monotone I-splines or
a normalized sigmoid. Missing entries are handled using a built-in partial
expectation-maximization procedure for matrix-variate data. The spatial
covariance and spatial factor analyzer models are described in Lu and
colleagues (2026a)
"Spatial Covariance Constraints for Gaussian Mixture Models"
<doi:10.48550/arXiv.2601.07979> and Lu and colleagues (2026b) "Mixtures of
spatial factor analyzers for tensor-variate data"
<doi:10.48550/arXiv.2607.07887>.
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