- Beta methods to support lme models, class
`lme`

for`residualDiagnostics()`

and`modelDiagnostics()`

with more planned in future updates.

Methods to support lme4 models, class

`merMod`

for`modelTest()`

,`modelDiagnostics()`

, and`APAStyler()`

.New vignette added showing sample use case of the package.

`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 by`lmer()`

.`modelCompare.merMod()`

A method to compare two models estimated by`lmer()`

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 plotting`meanDecompose()`

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