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
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