corplot() and functions that build on it (like plot() methods for SEMSummary()
now use a 45 degree angle for column labels to improve readability with long variable
names. As always this is a ggplot2 theme option and can be overwritten by the user.geom_tufterange() simple geom to add a left and bottom line that shows the observed range of non missing data.theme_tufte() simple mostly empty theme built off the minimal theme.is.naz() is a new function that is the testing counter part to naz.omit().
Notably, both is.naz() and naz.omit() both also identify and exclude
non finite values now. This is new behavior.egltable() has more error checks including for variables
that are all missing values. Frequency (percent) results are
now shown more clearly with a percent sign (%). This corresponds
to changes in the backend of egltable() to facilitate more
tests and descriptives to be calculated.diffCircular() calculates the circular difference
between two vectors.saveRDSfst() saves RDS files using fst for multithreaded compression.readRDSfst() reads RDS files using fst for multithreaded decompression.egltable() now correctly handles categorical variables by
a grouping variable, when the categorical variable is not
a factor class. Fixes a bug that could occur with
cells with zero frequencies when the variables were not
factors.winsorizor() would not properly check if a vector of percentiles
was passed. This is fixed and a new test added to prevent regression.meanCircular() would be off by pi in some circumstances. This has
been corrected and more test cases added.SEMSummary() pairwise correlations were based on the standardized
pairwise covariance matrix, which used the same standard deviation
for a variable regardless of the pair. This has now been fixed.stat_smooth() to reduce
messages about this.corplot() uses a more color blind friendly palette and defaults to
showing correlations and p-values.ggpubr instead of cowplot for themes and
arranging multiple plots.R required.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.data.table to the
DESCRIPTION and dependencies.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.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.