R-universe - lhzmix (LHZMix)

mpem - Matrix Partial EM for Incomplete Matrix-Normal Data

Fits single-component and finite-mixture Kronecker-structured matrix-normal models and imputes incomplete matrix-variate data using matrix partial expectation-maximization. General MPEM handles arbitrary missingness, while Rect-MPEM exploits rectangular structural missingness. The methods are described in Lu, Andrews and Browne (2026) "An Efficient EM Algorithm for Both Element-Wise and Structural Missingness in Matrix-Variate Normal Mixture Models" <doi:10.48550/arXiv.2609.00616>.

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openblascpp

2.70 score

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>.

Last updated

openblascpp

2.70 score 488 downloads