Package: mlmmm
Version: 0.2-1
Date: 2007-05-27
Title:  ML estimation under multivariate linear mixed models with missing values
Author: Recai Yucel <yucel@schoolph.umass.edu>.
Maintainer: Recai Yucel <yucel@schoolph.umass.edu>
Description: Computational strategies for multivariate linear mixed-effects
models with missing values, Schafer and Yucel (2002), Journal of Computational and Graphical Statistics, 11, 421-442.
License: The software may be distributed free of charge and
used by anyone if credit is given. It has been tested
fairly well, but it comes with no guarantees and the
authors assume no liability for its use or misuse
URL: http://www-unix.oit.umass.edu/~yucel/
Packaged: Tue May 29 11:36:50 2007; recai

Title: ML estimation under multivariate linear mixed models with
Description: Computational strategies for multivariate linear
mixed-effects models with missing values, Schafer and Yucel
(2002), Journal of Computational and Graphical Statistics, 11,
Packaged: 2010-07-06 21:05:56 UTC; recaiyucel

Authors@R: c(person("Laura Jamison", email = "lj5yn@virginia.edu", role = c("aut", "cre")),
person("Jessica Mazen", email = "mazenjes@gmail.com", role = c("aut")),
person("Erik Ruzek", email = "eruzek@virginia.edu", role = c("aut")),
person("Gus Sjobeck", email = "grs4de@virginia.edu", role = "ctb"))
Description: Multilevel models (mixed effects models) are the statistical tool of choice for analyzing multilevel data (Searle et al, 2009). These models account for the correlated nature of observations within higher level units by adding group-level error terms that augment the singular residual error of a standard OLS regression. Multilevel and mixed effects models often require specialized data pre-processing and further post-estimation derivations and graphics to gain insight into model results. The package presented here, 'mlmtools', is a suite of pre- and post-estimation tools for multilevel models in 'R'. Package implements post-estimation tools designed to work with models estimated using 'lme4''s (Bates et al., 2014) lmer() function, which fits linear mixed effects regression models. Searle, S. R., Casella, G., & McCulloch, C. E. (2009, ISBN:978-0470009598). Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014) <doi:10.18637/jss.v067.i01>.
Packaged: 2022-09-29 13:27:36 UTC; golinolab