plot.misty.object() for plotting a misty object.Added the argument factor.labels to the function df.head and df.tail.
Added the arguments filename, width, height, units, and dpi to the
functions aov.b, aov.w, multilevel.r2, multilevel.r2.manual, na.pattern,
test.levene, test.t, test.welch, and test.z.
The function df.rename can rename columns in a matrix or variables in a
data frame using old_name = new_name and using the functions toupper,
tolower, gsub, and sub similar to the rename function in the dplyr package.
multilevel.icc(), computation of the ICC(1) at
Level 2 was wrong in case of a three-level data when specifying type = "1b"
(thanks to David S. DeGarmo).center(), within-cluster centering of a Level-2
predictor variable in three-level was wrong (thanks to Stefanos Mastrotheodoros).df.head() and df.tail(), function could not
handle date and times.subset in the function df.subset is specified without quotation
marks in line with the argument subset in the function subset function.data in the functions as.na, na.as, center,
ci.cor, ci.cor, ci.mean, ci.median, ci.mean.w, ci.var, ci.sd,
cluster.scores, coding, cor.matrix, crosstab, descript, df.duplicated,
df.unique, df.move, df.subset, effsize, freq, item.alpha, item.cfa,
item.invar, item.omega, item.reverse, item.scores, lagged, multilevel.cfa,
multilevel.cor, multilevel.descript, multilevel.icc, multilevel.invar,
multilevel.omega, na.auxiliary, na.coverage, na.descript, na.indicator,
na.pattern, na.prop, na.test, rec, rwg.lindell, skewness
to the first position.x in the functions df.check, df.head, df.rename,
df.sort to data.x in the function multilevel.fit to model.alpha, ci.plot, plot.point, saveplot
in the functions ci.cor, ci.mean, ci.median, ci.prop, ci.var, and ci.sd
to hist.alpha, confint, point and filename.fill.col in the functions na.pattern to color.file in the functions blimp.plot, mplus.plot, and
na.pattern to filename.saveplot from the functions na.pattern.na in the functions na.indicator(),
to 1.ci.cor() for computing and potting Fisher z' confidence interval
for the Pearson product-moment correlation coefficient adjusted via sample joint
moments method or via approximate distribution method (Bishara et al., 2018),
Spearman's rank-order correlation coefficient with Fieller et al. (1957) standard
error, Bonett and Wright (2000) standard error or rank-based inverse normal (RIN)
transformation, and Kendall's Tau-b, and Kendall-Stuart's Tau-c. The function also
supports five types of bootstrap confidence intervalschr.trunc() for truncating a character vector, so that the number
of characters of each element of the character vector is always less than or equal
to the specified width.df.head() and df.tail() for printing the first or last rows
of a data frame and displaying only as many columns as fit on the console.df.check() which is a wrapper function around the functions
dim(), names(), df.head(), and df.tail().skewness and kurtosis can also compute Mardia's multivariate
skewness and kurtosis.ci.mean, ci.median, ci.prop, ci.var, and ci.sd can
also compute and plot bootstrap confidence intervals.sample to the function descript.append to the function check.outlier.sep and dec to the function read.data.gray1, gray2, and gray3 to the argument color of the
function chr.color.blimp.print(), function did print error messages.nrow, ncol and scales into facet.nrow, facet.ncol
and facet.scales in the functions blimp.plot() and mplus.plot().error.width into errorbar.width in the functions
aov.b(), aov.w(), result.lca(), test.t() and test.z().line.size and line.type into linewidth and linetype
in the functions test.t() and test.z().line.color1, line.color2, line.type1, line.type2,
line.width1, line.width2, bar.color, axis.size, strip.size, xlimits,
and ylimits into line.col1, line.col2, linetype1, linetype2,
linewidth1, linewidth2, bar.col, axis.text.size, strip.text.size, xlim,
and ylim in the function check.resid().line in the functions aov.w(),
to FALSE.size.mean, size.prop and size.cor
into one help page.missing in the functions multilevel.cfa(),
to "fiml".estimator in the functions multilevel.cor(),
to "MLR".na in the functions na.indicator(),
to 1.na.satcor(), cfa.satcor(), sem.satcor(), growth.satcor(),
and lavaan.satcor() to estimate a confirmatory factor analysis model, structural
equation model, growth curve model, or latent variable model in the lavaan package
using full information maximum likelihood (FIML) method to handle missing data
while automatically specifying a saturated correlates model to incorporate auxiliary
variables into a substantive model.read.data() to read data files in CSV, DAT, TXT, SPSS, Excel, or
Stata DTA format.print in the functions na.test(),
to little.mplus.plot(), which caused an error message when
requesting a loop plot by specifying plot = "loop".mplus.print(), which caused an error message when
printing a Mplus output for an automatic testing of measurement invariance.mplus and blimp do not require the ...; specification
in the VARIABLES section anymore when specifying variable names with the argument
data.labels in the function blimp.plot() to show parameter
labels in the facet labels.na.auxiliary() does not print full NA rows of the Cohen's d
matrix anymore.na.indicator() creates a missing data indicator matrix with
0 = observed and 1 = missing.na, append and name to the function na.indicator().mplus.print(), function did not the print input
result when specifying print = "all".blimp(), which caused an error message when
specifying a posterior = TRUE and saving the posterior distribution failed.blimp.print(), which caused an error message when
specifying a misty.object for the argument x.blimp.plot(), function did not save and plots
regardless of the setting of the argument saveplot.mplus.plot() to read a Mplus GH5 file to display trace plots,
posterior distribution plots, autocorrelation plots, posterior predictive check
plots, and loop plots.blimp.run() to run a group of Blimp models located within a single
directory or nested within subdirectories.blimp.print() to print a Blimp output file on the R console.blimp.plot() to read the posterior distribution for all parameters
to display trace plots and posterior distribution plots.blimp() to create and run a Blimp input to print the output onblimp.update() to update specific input command sections of a
misty.object of type blimp to create an updated Blimp input file, run the
updated input file, and print the updated Blimp output.mplus.bayes() to read a Mplus GH5 file and blimp.bayes()
to read the posterior distribution for all parameters to compute point estimates,
measures of dispersion, measures of shape, credible intervals, convergence and
efficiency diagnostics, probability of direction, and probability of being in
the ROPE for the posterior distribution for each parameter.na.test function provides Jamshidian and Jalalꞌs approach for testing the
missing completely at random (MCAR) assumption.clear() to clear the console equivalent to Ctrl + L in RStudio.chr.color() to add color and style to output texts on terminals
that support 'ANSI' color and highlight codes.default to the argument print of the descript function.comment to the mplus function.na.test performs Little's MCAR test using the mlest function
from the mvnmle package that can handle up to 50 variables instead of using
the prelim.norm function in the norm package that can only handle about 30
variables.na.pattern plots the missing data pattern when specifying
plot = TRUE and runs faster.na.auxiliary computes semi-partial correlations of an outcome variable
conditional on the predictor variables of a substantive model with a set of
candidate auxiliary variables to identify correlates of an incomplete outcome
variable as suggested by Raykov and West (2016)..print in the functions mplus.print,
to result.mplus.print does not print the section MODEL FIT INFORMATION
if the degrees of freedom is zero.run.mplus in the function mplus.lca() to mplus.run.ls.fit in the function multilevel.cfa()
to FALSE.mplus.update(), which caused an error message when
specifying output = FALSE.mplus.lca(), which caused an error message when
checking the input for the argument processors.item.cfa() and multilevel.cfa(), functions did
not allow specifying more than two residual covariances (thanks to Lydia Laninga-Wijnen).multilevel.descript(), average, minimum, and
maximum cluster size at Level 3 were calculated incorrectly.item.omega(), function did not provide item
statistics regardless of the print argument setting (thanks to Ainhoa Coloma Carmona).mplus.run() according to the latest 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 correlation 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-level 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 ordering.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.