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