Package: powerlmm
Type: Package
Title: Power Analysis for Longitudinal Multilevel Models
Version: 0.4.0
Authors@R: person("Kristoffer", "Magnusson", email = "hello@kristoffer.email",
    role = c("aut", "cre"))
Description: Calculate power for the 'time x treatment' effect
    in two- and three-level multilevel longitudinal studies with missing data.
    Both the third-level factor (e.g. therapists, schools, or physicians),
    and the second-level factor (e.g. subjects), can be assigned random slopes.
    Studies with partially nested designs, unequal cluster sizes,
    unequal allocation to treatment arms, and different dropout patterns
    per treatment are supported. For all designs power can be
    calculated both analytically and via simulations. The analytical
    calculations extends the method described in Galbraith et al. (2002)
    <doi:10.1016/S0197-2456(02)00205-2>, to three-level models.
    Additionally, the simulation tools provides flexible ways to investigate
    bias, Type I errors and the consequences of model misspecification.
License: GPL (>= 3)
URL: https://github.com/rpsychologist/powerlmm
BugReports: https://github.com/rpsychologist/powerlmm/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
Depends: R (>= 3.2.0)
Imports: stats, methods, parallel, lme4 (>= 1.1), Matrix, MASS, scales,
        utils
Suggests: testthat, dplyr, tidyr, knitr, rmarkdown, pbmcapply (>= 1.1),
        lmerTest (>= 2.0), ggplot2 (>= 2.2), ggsci, viridis, gridExtra,
        shiny (>= 1.0), shinydashboard
ByteCompile: true
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-08-14 08:12:29 UTC; kris
Author: Kristoffer Magnusson [aut, cre]
Maintainer: Kristoffer Magnusson <hello@kristoffer.email>
Repository: CRAN
Date/Publication: 2018-08-14 08:40:03 UTC
Built: R 3.6.0; ; 2019-04-27 02:40:17 UTC; unix
