| cevcmm-package | R Documentation |
Scalable inference for Varying Coefficient Mixed-Effects Models (VCMMs) with large, correlated random effects. The package implements:
Sufficient-statistics (SS) iterative estimator (Algorithm 1).
One-step communication-efficient surrogate likelihood (CSL) estimator.
SVD-stabilized variants for ill-conditioned random-effect Gram matrices.
Kronecker and separable covariance structures for origin-destination and group-shared random effects.
The package is in early development. See the project ROADMAP for the current status.
Maintainer: Lida Jalili lchalangarjalilideh1@gsu.edu
Authors:
Lida Jalili lchalangarjalilideh1@gsu.edu
Li-Hsiang Lin lhlin@gsu.edu
Jalili, L. and Lin, L.-H. (2025). Scalable and Communication-Efficient Varying Coefficient Mixed Effect Models: Methodology, Theory, and Applications.
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