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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.