{ "_id": "6ab100933d719c1f84085a2c", "Package": "MEMWAS", "Type": "Package", "Title": "Mixed-Effects Models with Autocorrelation Structures", "Version": "0.9.4.9002", "Date": "2026-08-20", "Authors@R": "c(\nperson(\"Enoch\", \"Kang\", role = c(\"aut\", \"cre\", \"trl\"),\nemail = \"y.enoch.kang@gmail.com\",\ncomment = c(ORCID = \"0000-0002-4903-942X\"))\n)", "Description": "Fits longitudinal mixed-effects models through a\nregistered 'C++' numerical backend. Supported serial covariance\nstructures include first-order autoregressive (AR(1)),\nexponential or Ornstein-Uhlenbeck, higher-order autoregressive\n(AR(p)), first-order autoregressive moving-average (ARMA(1,1)),\ncompound symmetry, Toeplitz, and unstructured covariance.\nSerial processes can be unified or attached independently to\nnumeric predictor loadings. Candidate temporal structures can\nbe ranked on a common sample by dependence-component grouped\ncross-validation, the Akaike information criterion, the\nBayesian information criterion, or log-likelihood. Clustered,\ncrossed, and nested random intercepts and slopes are assembled\njointly with diagonal or term-specific unstructured covariance.\nAvailable approximation methods include Laplace, saddlepoint\nlikelihood with latent Laplace integration, adaptive Gaussian\nquadrature, full-covariance Gaussian variational inference, and\npenalized quasi-likelihood. Penalized smooth mean terms include\nordinary and cyclic P-splines, factor-by and\nvarying-coefficient terms, tensor products, shrinkage smooths,\nand whole-term selection. Term-specific penalties, grouped\nfold-local smoothing selection, null-space constraints, and\nsmooth effective degrees of freedom remain separate from\nelastic-net coefficient shrinkage while the smooth mean and\nserial covariance are fitted jointly. Bootstrap resampling\npreserves the declared dependence components. The mixed-effects\nframework is inspired by Laird and Ware (1982)\n; generalized-model approximations are\ninspired by Breslow and Clayton (1993)\n; and serial covariance\nformulations are inspired by Pinheiro and Bates (2000)\n. 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