We extend 'dplyr' and 'fuzzyjoin' join functions with
features to preprocess the data, apply various data checks, and deal with
conflicting columns.
dplyr, glue, rlang, tidyselect, vctrs, purrr, tibble, tidyr,
cli, methods

Power Calculations for Longitudinal Multilevel Models
person("Kristoffer", "Magnusson", email = "hello@kristoffer.email",
role = c("aut", "cre"))
Calculate power for 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.
stats, lmerTest (>= 2.0), lme4 (>= 1.1), ggplot2 (>= 2.2),
ggsci, pbmcapply (>= 1.1), Matrix, MASS, gridExtra, scales,
utils, testthat
dplyr, tidyr, knitr, rmarkdown, viridis, shiny (>= 1.0),
shinydashboard

Power Analysis for Longitudinal Multilevel Models
Calculate power for 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.
stats, methods, parallel, lme4 (>= 1.1), Matrix, MASS, scales,
utils
testthat, dplyr, tidyr, knitr, rmarkdown, pbmcapply (>= 1.1),
lmerTest (>= 2.0), ggplot2 (>= 2.2), ggsci, viridis, gridExtra,
shiny (>= 1.0), shinydashboard