# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "MEMWAS" in publications use:' type: software license: GPL-3.0-or-later title: 'MEMWAS: Mixed-Effects Models with Autocorrelation Structures' version: 0.9.4.9002 doi: 10.32614/CRAN.package.MEMWAS abstract: 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) ; generalized-model approximations are inspired by Breslow and Clayton (1993) ; and serial covariance formulations are inspired by Pinheiro and Bates (2000) . The run-time fitting interface imports no third-party 'R' packages. authors: - family-names: Kang given-names: Enoch email: y.enoch.kang@gmail.com orcid: https://orcid.org/0000-0002-4903-942X repository: https://enochkang.r-universe.dev commit: b1d8658c7e524b9d30b42592785af7bc646e4bf2 date-released: '2026-08-20' contact: - family-names: Kang given-names: Enoch email: y.enoch.kang@gmail.com orcid: https://orcid.org/0000-0002-4903-942X