ranefdata()
to improve visibility of points and lines.evaluateLags() function to evaluate how far back lags should go to improve model fit.ranefdata() function to extract random effects from a brms model and
return and plot them. Designed to make caterpilar plots with posterior summaries.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.weighted.sma function to calculate weighted simple
moving averages.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