MEMWAS: Mixed-Effects Models with Autocorrelation Structures
Fits longitudinal generalized mixed-effects models through the
'MEMWAS' interface and 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 primary-cluster 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. Subject-grouped tuning requires
every validation fold to succeed and supports fold-local nonlinear
screening, bootstrap inference, prediction inference, and effective degrees
of freedom for penalized information criteria. The mixed-effects framework
follows Laird and Ware (1982) <doi:10.2307/2529876>; generalized-model
approximations follow Breslow and Clayton (1993)
<doi:10.1080/01621459.1993.10594284>; and serial covariance formulations
follow Pinheiro and Bates (2000) <doi:10.1007/b98882>. The run-time
fitting interface imports no third-party 'R' packages.
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cre, trl]