Changelog
Source:NEWS.md
multilevelTools 0.1.3
Bug Fixes

modelTest()
no longer fails for models with a continuous x categorical interaction. Estimates for dropping the “simple” effect of the continuous variable are still not calculable, but the rest of the calculations are still performed and that line is simply set to NA.
multilevelTools 0.1.1
CRAN release: 20200304
New Features
 Beta methods to support lme models, class
lme
forresidualDiagnostics()
andmodelDiagnostics()
with more planned in future updates.
multilevelTools 0.1.0
New Features
Methods to support lme4 models, class
merMod
formodelTest()
,modelDiagnostics()
, andAPAStyler()
.New vignette added showing sample use case of the package.
Ported Features
omegaSEM()
Function that calculates coefficient omega for measuring internal consistency reliability. Works for two level models and returns within and between level omega values.R2.merMod()
A method to calculate the marginal and conditional variance accounted for by a model estimated bylmer()
.modelCompare.merMod()
A method to compare two models estimated bylmer()
include significance tests and effect sizes for estimates of the variance explained.iccMixed()
A function to calculate the intraclass correlation coefficient using mixed effects models. Works with either normally distributed outcomes or binary outcomes, in which case the latent variable estimate of the ICC is computed.nEffective()
Calculates the effective sample size based on the number of independent units, number of observations per unit, and the intraclass correlation coefficient.acfByID()
Calculates the lagged autocorrelation of a variable by an ID variable and returns a data.table for further use, such as examination, summary, or plottingmeanDecompose()
function added to decompose multilevel or repeated measures data into means and residuals.meanDeviations()
A simple function to calculate means and mean deviations, useful for creating between and within versions of a variable in a data.table