c(person(given = "Louis",
family = "Rocconi",
role = c("aut", "cre"),
email = "lrocconi@utk.edu",
comment = c(ORCID = "0000-0003-0693-0957")),
person(given = "Anthony",
family = "Schmidt",
role = "aut",
email = "aschmi11@utk.edi",
comment = c(ORCID = "0000-0003-4478-0638")))
A collection of miscellaneous helper function for running multilevel/mixed models in 'lme4'. This package aims to provide functions to compute common tasks when estimating multilevel models such as computing the intraclass correlation and design effect, centering variables, estimating the proportion of variance explained at each level, pseudo-R squared, random intercept and slope reliabilities, tests for homogeneity of variance at level-1, and cluster robust and bootstrap standard errors. The tests and statistics reported in the package are from Raudenbush & Bryk (2002, ISBN:9780761919049), Hox et al. (2018, ISBN:9781138121362), and Snijders & Bosker (2012, ISBN:9781849202015).
clubSandwich, testthat (>= 3.0.0), knitr

Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel (2018) <arXiv:1210.0870v9>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.
MASS, gsl, norm, cat, mix, Matrix, stats, utils

Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2019) <arXiv:1210.0870v10>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.
MASS, gsl, norm, cat, mix, Matrix, stats, utils, nlme

Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.