## LMMstar: Repeated Measurement Models for Discrete Times

Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers" taught by the section of Biostatistics of the University of Copenhagen. It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...). Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom. Normalized residuals are provided to assess model misspecification. Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients. Predictions can be computed conditional to covariates only or also to outcome values.

 Version: 1.1.0 Depends: R (≥ 3.5.0) Imports: copula, doParallel, foreach, ggplot2, grid, lava, Matrix, multcomp, nlme, numDeriv, parallel, rlang Suggests: asht, data.table, ggh4x, ggpubr, lattice, mvtnorm, lme4, lmerTest, mice, nlmeU, optimx, pbapply, psych, Publish, qqtest, R.rsp, reshape2, rmcorr, scales, testthat Published: 2024-05-12 Author: Brice Ozenne [aut, cre], Julie Forman [aut] Maintainer: Brice Ozenne BugReports: https://github.com/bozenne/LMMstar/issues License: GPL-3 URL: https://github.com/bozenne/LMMstar NeedsCompilation: no Citation: LMMstar citation info Materials: NEWS CRAN checks: LMMstar results

#### Documentation:

 Reference manual: LMMstar.pdf Vignettes: LMMstar: overview LMMstar: partial residuals