MEMWAS: Mixed-Effects Models with Autocorrelation Structures
Fits longitudinal mixed-effects models through a registered 'C++'
numerical backend. Supported serial covariance structures include first-order
autoregressive (AR(1)), exponential or Ornstein-Uhlenbeck, higher-order
autoregressive (AR(p)), first-order autoregressive moving-average (ARMA(1,1)),
compound symmetry, Toeplitz, and unstructured covariance. Serial processes
can be unified or attached independently to numeric predictor loadings.
Candidate temporal structures can be ranked on a common sample by
dependence-component grouped cross-validation, the Akaike information
criterion, the Bayesian information criterion, or log-likelihood. Clustered,
crossed, and nested random intercepts and slopes are assembled jointly with
diagonal or term-specific unstructured covariance. Available approximation
methods include Laplace, saddlepoint likelihood with latent Laplace
integration, adaptive Gaussian quadrature, full-covariance Gaussian variational
inference, and penalized quasi-likelihood. Penalized smooth mean terms
include ordinary and cyclic P-splines, factor-by and varying-coefficient terms,
tensor products, shrinkage smooths, and whole-term selection. Term-specific
penalties, grouped fold-local smoothing selection, null-space
constraints, and smooth effective degrees of freedom remain separate from
elastic-net coefficient shrinkage while the smooth mean and serial covariance
are fitted jointly. Bootstrap resampling preserves the declared dependence
components. The mixed-effects framework is inspired by Laird and Ware (1982)
<doi:10.2307/2529876>; generalized-model approximations are inspired by
Breslow and Clayton (1993) <doi:10.1080/01621459.1993.10594284>; and serial
covariance formulations are inspired by Pinheiro and Bates (2000)
<doi:10.1007/b98882>. The run-time fitting interface imports no third-party
'R' packages.
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