multilevelTools 0.2.1

Changes

• Improved documentation links to functions in other packages.

multilevelTools 0.2.0

New Features

• 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.

Changes

• Moved testthat to 3rd edition.
• Updated GitHub actions

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.

Changes

• moved to testthat 3rd edition
• moved CI to GitHub actions
• use preferably for package website

multilevelTools 0.1.2

New Features

• New weighted.sma function to calculate weighted simple moving averages.

multilevelTools 0.1.1

New Features

• Beta methods to support lme models, class lme for residualDiagnostics() and modelDiagnostics() with more planned in future updates.

multilevelTools 0.1.0

New Features

• Methods to support lme4 models, class merMod for modelTest(), modelDiagnostics(), and APAStyler().

• 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 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