`modelTest()`

now works correctly when there are interaction terms with categorical variables and when most “on-the-fly” transformations are performed, such as`log()`

etc. Does not work when new variables that are a composite of multiple variables are created, e.g.,`I(hp + wt)`

, but a more informative error message is given.- Added the correct version requirements for
`data.table`

to the DESCRIPTION and dependencies.

- Polished methods for functions, including
`APAStyler()`

methods.

`residualDiagnostics.lm()`

and so too`modelDiagnostics.lm`

would return the index of extreme values based on the complete data used for modelling, not of the original input dataset. This made it difficult to identify and remove extreme values in subsequent model runs. This is now corrected.

- This version includes a significant re-write of the package that results in many of the previous user-facing functions changing names and arguments. This will likely break old code. This re-write was necessary to help standardize functions and arguments, and to make functions more robust. Many functions are now generics with specific methods written. Further, some functions previously bundled into
`JWileymisc`

have been separated into other packages, including the new`extraoperators`

package, covering binary operators, and a package for diagnostics on mixed models. See the new vignettes added to the package for examples of current practice in using`JWileymisc`

.

`egltable()`

has added statistical tests for paired data. For continuous, parametric paired data, a pseudo Cohen’s d is calculated on the change scores.

`omegaSEM()`

Function that calculates coefficient omega for measuring internal consistency reliability. Works for two level models and returns within and between level omega values.`egltable()`

Function has added effect sizes when multiple groups are compared including Cohen’s d for two groups, eta-squared for multiple groups, and phi for categorical variables.

`testdistr()`

now only finds extreme values for the right tail of a chi-square distribution.`.detailedTestsVGLM()`

now identifies levels of the outcome correctly.

`detailedTests()`

is now more generic and dispatches to`.detailedTestsLMER()`

or`.detailedTestsVGLM()`

to provide detailed tests for both linear mixed effects models and multinomial logistic regression models fit by`vglm()`

.`ezMULTINOM()`

is now deprecated in favor of the new, more generic`detailedTests()`

.

`testdistr()`

now creates more appropriate plots for discrete distributions including the Poisson and Negative Binomial.`moments()`

now updated to accomodate changes in the lavaan package (thanks to Yves Rosseel)`TukeyHSDgg()`

updated to use the emmeans package instead of the now defunct lsmeans package.`formatLMER()`

returned the lower confidence interval twice instead of the lower and upper confidence interval. This is now fixed.

`R2LMER()`

A simple function to calculate the marginal and conditional variance accounted for by a model estimated by`lmer()`

.`compareLMER()`

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

include significance tests and effect sizes for estimates of the variance explained.`detailedTests()`

A function to compute detailed tests on a model estimated from`lmer()`

including confidence intervals for parameters, significance tests, where possible, overall model fit, and effect sizes for the model and each variable.`formatLMER()`

A function to nicely format detailed model results, possibly from multiple models. Requires results from`detailedTests()`

based on`lmer()`

models, at the moment.`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`meanDeviations()`

A simple function to calculate means and mean deviations, useful for creating between and within versions of a variable in a data.table`as.na()`

function added to convert data to missing (NA) while preserving the class/type of the data (useful for data.table).`meanDecompose()`

function added to decompose multilevel or repeated measures data into means and residuals.`timeshift()`

function added to center a time variable at a new zero point. Useful when times may start and end off the standard 24 hour period (e.g., 11am to 2am, which technically fall on different dates).`intSigRegGraph()`

function added to graph regions of significance from interactions with linear models as well as the mostly helper function,`findSigRegions()`

.`ezMULTINOM()`

new function added to make running multinomial logistic regression easy in R, along with all pairwise contrasts and omnibus tests of statistical significance.`testdistr()`

function expanded to cover multivariate normal data, and the old`mvqq()`

function is now deprecated.`testdistr()`

includes optional robust estimates for univariate and multivariate normal data`formatHtest()`

gains support for pearson, kendal, and spearman correlations from the`cor.test()`

function`logicals`

A series of support functions for findings values in a particular range, such as`%gele%`

for values greater than or equal to the min and less than or equal to the max as well as to automatically subset the data when prefixed with an s,`%sgele%`

`%sin%`

etc.

`winsorizor()`

now properly handles atomic data. Fixes an issue where variables in a data table would be atomic after calling the`scale()`

function and`winsorizor()`

would fail.`egltable()`

now works with data.tables

`testdistr()`

function to plot data against different theoretical distributions using`ggplot2`

. A sort of generalized`qqnorm()`

allowing other distributions besides the normal distribution.`winsorizor()`

Function moved from`pscore`

package. Sets any values beyond specific quantiles of the empirical data to the specified quantiles. Can work on vectors, data frames, or matrices.

Initial release to CRAN.