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mplus.run() according to the
lastest version of the function runModels() in the
MplusAutomation package.mplus.print(), function did
not print result of a misty.object of type mplus.mplus.print() for printing a Mplus output
file on the R console.mplus() to create and run a Mplus input to
print the output on the console.update.mplus() to update specific Mplus
input command sections in the mplus object, run the updated
input file, and print the output on the console.chr.grep() and chr.grepl()
for multiple pattern matching, i.e., grep() and
grepl() functions for matching a vector of character
strings.write.mplus() is not restricted to
variable names with up to 8 characters anymore.run.mplus() to
mplus.run().posthoc in
the functions aov.b(), aov.w() and
test.welch() to FALSE.replace from
modifiedDate to modified in the functions
mplus.lca() and mplus.run().showOutput into
show.out and replaceOutfile into
replace.out in the function mplus.run().message to the function
mplus.run().test.welch(), function did
not print post hoc tests when specifying
posthoc = TRUE.result.lca(), function
excluded all outputs which involved the word ERROR even
though results were available (thanks to Michael Weber).multilevel.fit(), function
used the number of observations at the Within level instead of the
Between level for computing RMSEA at the Between Level (thanks to
Maurizio Sicorello).descript() which caused an
error message when specifying a split variable.robust.coef() which caused
an error message in the presence of missing data on predictor
variables.multilevel.icc() and
multilevel.descript() which caused an error message in when
specifying a tibble instead of a data frame (thanks to Tanja Held).mplus.lca(), the argument
processors allows to specify the number of processors and
threads separately.item.omega(), residual covariances can
be specified when type = "categ".., +, -, ~,
:, ::, functions which caused an warning
message.center(),
multilevel.icc(), and multilevel.descript()
which caused an error message in three-level data with ambiguously coded
cluster variables common in longitudinal data.multilevel.descript() to take into
account missing values, e.g., No. of cases and
No. of clusters show the number observations and clusters
after excluding missing values.write.sav() do not
require specifying all three columns label,
values, and missing anymore.na to the function
read.mplus().freq(), function did not
provide an output.df.subset() for subsetting data frames
using the operators ., +, -,
~, :, ::, and !
similar to functions from the R package tidyselect.lagged() to compute lagged values of
variables.df.move() to move variable(s) in a data
frame.read.dta() and write.dta()
to read and write Stata DTA files.coding() to code categorical variables,
i.e., dummy, simple, unweighted and weighted effect, repeated, forward
Helmert, reverse Helmert, and orthogonal polynomial coding.effsize() to compute effect sizes for
categorical variables, i.e., (adjusted) phi coefficient,
(bias-corrected) Cramer’s V, (bias-corrected) Tschuprow’s T, (adjusted)
Pearson’s contingency coefficient, Cohen’s w, and Fei.script.copy() to save a copy of the
current script in RStudio with the current date and time.as.na(), na.as()``center(),
ci.mean(), ci.mean.w(),
ci.median(), ci.prop(), ci.var(),
ci.sd(), cluster.scores(),
cor.matrix(), crosstab(),
descript(), freq(), item.alpha(),
item.cfa(), item.invar(),
item.omega(), item.reverse(),
item.scores(), multilevel.cfa(),
multilevel.cor(), multilevel.descript(),
multilevel.fit(), multilevel.icc(),
multilevel.invar(), multilevel.omega(),
na.auxiliary(), na.coverage(),
na.descript(), na.indicator(),
na.pattern(), na.prop(),
na.test() rec(), rwg.lindell(),
skewness(), and kurtosis() provide the
argument ... instead of the argument x to
specify variables from the data frame specified in data
using the operators ., +, -,
~, :, ::.multilevel.icc() computes intraclass
corelation coefficients in three-level data.multilevel.descript() computes multilevel
descriptive statistics in three-level data.center() centers predictor variables in
three-level data.na.descript() provides descriptive statistics
for missing data in two-level and three-lavel data.cor.matrix() computes tetrachoric and
polychoric correlation coefficients.write and append to
all functions providing a print function to save the print output into a
text file.names in
the function rec() to .e.label and
labels in the read.sav function to
FALSE.value in the function
na.as() to na to make it consistent with the
arguments of the function as.na().resid.cov in the function
item.omega() to resocv to make it consistent
with the arguments of the functions item.cfa() and
multilevel.cfa().names in the functions
center, cluster.scores,
item.reverse, and rec to name to
make it consistent with the arguments of the functions
item.scores(), na.prop(), and
lwg.lindell().x and ... in the
functions df.duplicated() and df.unique()to
... and data to make it consistent with all
other functions using the ... argument.as.na and
na.as into one help page.script.open,
script.close, and script.save into one help
page.skewness and
kurtosis into one help page.ci.mean and
ci.median into one help page.ci.var and
ci.sd into one help page.shift() and replaced it by the
function lagged().dummy.c() and replaced it by the
function coding()cor.phi(),
cor.cont(), cor.cramer(), and
eta.sq() and replaced them by the function
effsize().cor.poly() and integrated
polychoric correlation coefficient into the function
cor.matrix().multilevel.descript(),
function led to a node stack overflow.shift() to compute lagged or leading
values of a vector.libraries(), version of the
packages were not correctly displayed.test.welch(), to remove
errors for r-devel from a recent change in r-devel.group.ind to the function
result.lca() to specify. latent class indicators as
grouping variable in the bar charts.mplus.lca() can be used to conduct latent
class analysis with count, unordered categorical, and ordered
categorical indicator variables.result.lca() can be used to save bar charts
with error bars for confidence intervals for each of the latent class
solutions.dominance.manual(),
function provided the wrong rank orderning.mplus.lpa() and
results.lpa() to mplus.lca() and
results.lca().item.invar() for evaluating configural,
metric, scalar, and strict between-group or longitudinal (partial)
measurement invariance.robust.coef() for computing
heteroscedasticity-consistent standard errors and significance values
for linear models estimated by using the lm() function and
generalized linear models estimated by using the glm()
function.dominance() for linear models estimated by
using the lm() function and dominance.manual()
to conduct dominance analysis based on a (model-implied) correlation
matrix of the manifest or latent variables.check.resid() for performing residual
diagnostics to detect nonlinearity (partial residual or
component-plus-residual plots), nonconstant error variance (predicted
values vs. residuals plot), and non-normality of residuals (Q-Q plot and
histogram with density plot).mplus.lpa() for writing Mplus input files
for conducting latent profile analysis based on six different
variance-covariance structures.result.lpa() for creating a summary result
table for latent profile analysis from multiple Mplus output files
within subfolders.order to the function
multilevel.cor() to order variables in the output table so
that variables specified in the argument between are shown
first.multilevel.cfa() and multilevel.invar().item.cfa(), multilevel.cfa(), and
multilevel.invar().write.result() can also write results based on
the return object of the std.coef function.min.value in the function
item.cfa(), multilevel.cfa(), and
multilevel.invar() to mod.minval and changed
the default setting to 6.63.r2mlm from the
Imports field in the DESCRIPTION due to
dependencies issues.multilevel.descript() can also deal with
between-cluster variables by reporting means and standard deviations at
the cluster level.print to the function
multilevel.descript() to request standard deviation of the
variance components.multilevel.fit() for computing
simultaneous and level-specific model fit information for a fitted
multilevel model containing no cross-level constraints from the R
package lavaan.multilevel.cfa() for conducting multilevel
confirmatory factor analysis using the R package lavaan to investigate
four types of constructs, i.e., within-cluster, shared, configural, and
simultaneous shared and configural cluster constructs.multilevel.invar() for evaluating
configural, metric, and scalar cross-level measurement invariance using
multilevel confirmatory factor analysis.multilevel.omega() for computing point
estimate and Monte Carlo confidence interval for the multilevel
composite reliability defined by Lai (2021) for a within-cluster
construct, shared cluster-level construct, and configural cluster
construct.multilevel.cor(), e.g., warning message is printed when
absolute correlations are greater than 1.cluster in the function
multilevel.cor(), multilevel.descript(), and
multilevel.icc() can also be specified using the variable
name of the cluster variable in x.item.cfa() function, e.g.,
loglikelihood and information criteria are shown above chi-square test
of model fit and label Ad Hoc changed to
Scaled.multilevel.cor(), which
caused an error message (thanks to Richard Janzen).libraries() to load and attach multiple
add-on packages at once.check.outlier() computes statistical
measures for leverage, distance, and influence for linear models
estimated by using the lm() functionwrite.result(), result tables are
in line with the arguments print, tri,
digits, p.digits, and icc.digits
specified in the object x (thanks to Stefan Kulakow).crosstab() displays marginal row-wise,
column-wise, and total percentages in the output (thanks to Joachim
Fritz Punter and Lisa Bucher). Note that the function now also returns
the crosstable in the list element result$crosstab of the
return object .Value sections in the documentation of the
functions.weighted in
the test.t and the na.auxiliary function to
FALSE in line with the recommendation by Delacre et
al. (2021).collin.diag() to
check.collin().read.mplus(), an error
message was printed if comments in the Mplus input file contains special
characters (e.g., ä, ü, ö).std.coef(), the function
was not applicable to predictors specified as character vector or
factor.script.close(),
script.new(), script.open(), and
script.save() to close, open, and save R scripts in
RStudio.setsource() to set the working directory
to the source file location in RStudio equivalent to using the menu item
Session - Set Working Directory - To Source File Location.restart() to restart the RStudio session
equivalent to using the menu item Session - Restart R.multilevel.r2.manual() to compute
R-squared measures by Rights and Sterba (2019) for multilevel and linear
mixed effects models by manually inputting parameter estimates.center(), cluster.scores(),
rec(), and item.reverse() can be applied to
more than one variable at once.aov.w() for performing repeated measures
analysis of variance (within-subject ANOVA) including paired-samples
t-tests for multiple comparison, descriptive statistics, effect size
measures, and a plot showing error bars for within-subject confidence
intervals.ci.mean.w() for computing
difference-adjusted Cousineau-Morey within-subject confidence
intervals.ci.mean.diff() computes the confidence
interval for the difference for an arithmetic mean in a one-sample
design.aov.b(), test.t(),
test.welch(), and test.z() plot
difference-adjusted confidence intervals in two-sample design by
default.jitter.height to the functions
aov.b(), test.levene(), test.t(),
aov.welch(), and test.z().adjust to the function
ci.mean(), to apply difference-adjustment for the
confidence interval.test.t() displays the confidence interval for
the mean difference in the one-sample t-test.test.t(), result table
provided by the function did not display the confidence interval
correctly.aov.b() for performing between-subject
analysis of variance including Tukey HSD post hoc test for multiple
comparison.as.na() is also applicable to arraysplot and arguments for various
graphical parameters for plotting results to the functions
test.levene(), test.t(),
test.welch(), and test.z().write for writing results into an
Excel file to the functions cor.matrix(),
crosstab(), descript(), freq(),
item.alpha(), item.cfa(),
item.omega(), multilevel.cor(),
multilevel.descript(), na.coverage(),
na.descript(), and na.pattern()posthoc for conducting Games-Howell
post hoc test for multiple comparison to the functions
test.welch().item.cfa() for conducting confirmatory
factor analysis using the R package lavaan.write.result() can also write results based on
the return object of the item.cfa() function.exclude of the function freq()
can also be set to FALSE.multilevel.cor() to
make it consistent with the output of the function
item.cfa().na.omit in the function
multilevel.cor() to missing to make it
consistent with the arguments of the function
item.cfa().estimator
in the function multilevel.cor() to ML, so
that full information maximum likelihood method is used for dealing with
missing data.multilevel.cor(), function
did not use Huber-White robust standard errors, but conventional
standard errors when specifying estimator = "MLR".multilevel.r2() for computing R-squared
measures for multilevel and linear mixed effects models.write.xlsx() for writing Excel files
(.xlsx).write.result() for writing results of a
misty object into an Excel file.multilevel.descript().round to the function
freq() for rounding numeric variables.na.test() function when
running into numerical problems.sig in the
functions cor.matrix() and multilevel.cor() to
FALSE.collin.diag() function.print.misty.object(),
function did not print the result object of the the function
crosstab() correctly when requesting percentages.multilevel.cor() for computing the
within-group and between-group correlation matrix using the lavaan
package.na.test() for performing Little’s missing
completely at random (MCAR) test.indirect() for computing confidence
intervals for the indirect effect using the asymptotic normal method,
the distribution of the product method, and the Monte Carlo method.multilevel.indirect() for computing
confidence intervals for the indirect effect in a 1-1-1 multilevel
mediation model using the Monte Carlo method.cor.matrix() highlights statistically
significant correlation coefficients in boldface.cor.matrix() shows the results in a table when
computing a correlation coefficient for two variables.stat) and degrees of freedom
(df) to the argument print in the function
cor.matrix().continuity for continuity correction
to the function cor.matrix() for testing Spearman’s
rank-order correlation coefficient and Kendall’s Tau-b correlation.cor.matrix() when computing Spearman’s rank-order
correlation coefficient or Kendall’s Tau-b correlation.group in the functions
center(), group.scores(),
multilevel.descript(), multilevel.icc(), and
rwg.lindell() to cluster.group.scores() to
cluster.scores().cor.matrix(), function did
not print sample sizes when specifying a grouping variable and using
listwise deletion.write.mplus() writes a Mplus input template
with variables names specified in the DATA command along with the
tab-delimited data file by default.print() in the
write.mplus() function.weighted in
the test.welch() function into FALSE following
the recommendation by Delacre et al. (2021).cohens.d(), function
printed warning messages of the pt() function.cohens.d(), function could
not deal with more than one variable in a one-sample design.test.t() for performing one-sample,
two-sample, and paired-sample t-tests including Cohen’s d effect size
measure.test.welch() for performing Welch’s t-test
including Cohen’s d effect size measure and Welch’s ANOVA including
\(\eta^2\) and \(\omega^2\) effect size measures.print
in the function descript().format, label,
labels, missing to the function
read.sav() to remove variable formats, variable labels,
value labels, value labels for user-defined missings, and widths from
attributes of the variable.item.reverse() can also be applied to to items
with non-integer values.cor.matrix() when
specifying a grouping variable comprises the combined results of both
groups in the matrices.read.mplus() can also deal with consecutive
variables (e.g., x1-x5).group and split arguments to the
function cohens.d().test.z function.cohens.d() computes various kinds of Cohen’s
d, Hedges’ d, and Glass’s \(\Delta\)
including confidence intervals, e.g., weighted and unweighted pooled
standard deviation in a two-sample design, with and without controlling
for the correlation between the two sets of measurement in a
paired-sample design, or with and without the small-sample correction
factor.alpha.coef() to
item.alpha(), cont.coef() to
cor.cont(), cramers.v() to
cor.cramer(), levenes.test() to
test.levene(), mgsub() to
chr.gsub(), omega.coef() to
item.omega(), reverse.item() to
item.reverse(), phi.coef() to
cor.phi(), poly.cor() to
cor.poly(), scores() to
item.scores(), stromit() to
chr.omit(), trim() to chr.trim(),
z.test() to test.z(),use in the
cor.matrix() function into na.omit.method in
the functions multilevel.descript() and
multilevel.icc() to "lme4"; if the lme4
package is not installed, "aov" will be used.ci.mean.diff() and
ci.mean.prop() when computing confidence intervals in
two-sample designs, i.e., results are divided in two rows according to
the grouping variable.ci.mean.diff() and
ci.mean.prop() when computing confidence intervals in
paired-sample designs, i.e., output reports the number of missing data
pairs (nNA), instead of number of missing values for each
variable separately (nNA1 and nNA2).descript() when
specifying the argument levenes.test(), i.e., duplicated
labels in the column group or variable are not
shown.cohens.d() into a generic
function with the methods cohens.d.default() and
cohens.d.formula().hypo and descript to the
functions test.levene() and test.z().freq,
descript, and crosstab function.as.na in the as.na()
function into na.center() which caused an
error message in case of groups with only one observation when trying to
apply group mean centering.center() which caused an
error message when trying to apply grand mean centering of a Level 1
predictor.cohens.d(), an error
message was printed in the between subject design whenever specifying a
grouping variable with missing values.cor.matrix(), which caused
an error when using listwise deletion for missing data while specifying
a grouping variable.descript(), which caused an
error message when selection only one or two argument statistical
measures using the argument print.freq(), where the argument
split was broken.test.zz(), where the
alternative hypothesis was displayed wrong when specifying
alternative = "greater" or
alternative = "less".collin.diag() for collinearity diagnostics
including tolerance, (generalized) standard error inflation factor,
(generalized) variance inflation factor, eigenvalues, conditional
indices, and variance proportions for linear, generalized linear, and
mixed-effects models.std.coef() for computing standardized
coefficients (StdX, StdY, and StdYX) for linear models estimated by
using the lm() function.mgsub() for multiple pattern matching and
replacements, i.e., gsub() function for matching and
replacing a vector of character strings.df.duplicated() and
df.unique() extracting duplicated or unique rows of a
matrix or data frame.read.xlsx(), default
setting of the argument progress was wrong.print.misty.object().z.test() for performing one sample, two
sample, and paired sample z-test.omega.coef() does not access internal slots of
a fitted lavaan object anymore (requested by Yves Rosseel).levenes.test().size.mean(),
size.prop(), and size.cor() to include greek
letters.theta in the
size.mean() function into delta.ci.mean(), ci.mean.diff(),
ci.median(), ci.prop(),
ci.prop.diff(), ci.sd(), ci.var()
for computing confidence interval for the arithmetic mean, the
difference in arithmetic means, the median, the proportion, the
difference in proportions, the variance, and the standard
deviation.levenes.test() for conducting Levene’s
test for homogeneity of variance.omega.coef() for computing coefficient
omega (McDonald, 1978), hierarchical omega (Kelley &
Pornprasertmanit, 2016), and categorical omega (Green & Yang,
2009).read.xlsx() for reading Excel files
(.xlsx).coef.alpha().cor.matrix().as.na() can also replace user-specified values
with missing values in lists.use in the
alpha.coef() function into a logical argument
na.omit.pval.digits in the
cor.matrix() function into p.digits.print.cont.coef(),
print.cramers.v(), print.na.auxiliary(),
print.na.coverage(), print.phi.coef(), and
print.poly.cor() into
print.square.matrix()is.vector()
function was used to test if an object is a vector. Instead
is.atomic() function is used to test if an object is a
vector.as.na(), function converted
strings in data frames to factors.trim() for removing whitespace from start
and/or end of a string. Note that this function is equivalent to the
function trimws() in the base package.
However, the trimws() function fails to remove whitespace
in some instances.cohens.d(), function
returned NA for Cohen’s d in within-subject design in the
presence of missing valuesalpha.coef(), function did
not provide any item statistics irrespective of the argument
printas.na(), function always
generated a warning message irrespective of the argument
as.na.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.