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cff-version: 1.2.0
message: 'To cite package "mlmtools" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'mlmtools: Multi-Level Model Assessment Kit'
version: 1.1.0
doi: 10.32614/CRAN.package.mlmtools
abstract: 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) <https://doi.org/10.18637/jss.v067.i01>.
authors:
- name: Laura Jamison
  email: lj5yn@virginia.edu
- name: Jessica Mazen
  email: mazenjes@gmail.com
- name: Erik Ruzek
  email: eruzek@virginia.edu
repository: https://lkjamison.r-universe.dev
commit: a6bb2233c3460373eb7d62479f6b248b91af7e7d
contact:
- name: Laura Jamison
  email: lj5yn@virginia.edu