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misty (version 0.8.3)

Miscellaneous Functions 'T. Yanagida'

Description

Miscellaneous functions for (1) data handling (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3) missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), (4) multilevel data (e.g., multilevel descriptive statistics, within-group and between-group correlation matrix, multilevel confirmatory factor analysis, level-specific fit indices, cross-level measurement equivalence evaluation, multilevel composite reliability, and multilevel R-squared measures), (5) item analysis (e.g., confirmatory factor analysis, coefficient alpha and omega, between-group and longitudinal measurement equivalence evaluation), (6) statistical analysis (e.g., bootstrap confidence intervals, collinearity and residual diagnostics, dominance analysis, between- and within-subject analysis of variance, latent class analysis, t-test, z-test, sample size determination), and (7) functions to interact with 'Blimp' and 'Mplus'.

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Version

Install

install.packages('misty')

Monthly Downloads

1,619

Version

0.8.3

License

MIT + file LICENSE

Maintainer

Takuya Yanagida

Last Published

August 2nd, 2026

Functions in misty (0.8.3)

blimp.update

Blimp Input Updating
blimp

Create, Run, and Print Blimp Models
blimp.plot

Blimp Trace Plots and Posterior Distribution Plots
blimp.run

Run Blimp Models
center

Centering Predictor Variables in Single-Level and Multilevel Data
boot.bs

Bollen-Stine Bootstrapping with Incomplete Data
blimp.bayes

Blimp Summary Measures, Convergence and Efficiency Diagnostics
aov.w

Repeated Measures Analysis of Variance (Within-Subject ANOVA)
aov.b

Between-Subject Analysis of Variance
blimp.print

Print Blimp Output
chr.trim

Trim Whitespace from String
check.resid

Residual Diagnostics for Linear, Multilevel and Mixed-Effects Models
chr.grep

Multiple Pattern Matching
chr.trunc

Truncate a Character Vector to a Maximum Width
chr.omit

Omit Strings
check.outlier

Statistical Measures for Leverage, Distance, and Influence
chr.gsub

Multiple Pattern Matching And Replacements
chr.color

Colored and Styled Terminal Output Text
check.collin

Collinearity Diagnostics
ci.cor

(Bootstrap) Confidence Intervals for Correlation Coefficients
ci.mean

(Bootstrap) Confidence Intervals for Arithmetic Means and Medians
cluster.rwg

Lindell, Brandt and Whitney (1999) r*wg(j) Within-Group Agreement Index for Multi-Item Scales
ci.mean.w

Within-Subject Confidence Interval for the Arithmetic Mean
cluster.scores

Cluster Scores
ci.prop

(Bootstrap) Confidence Intervals for Proportions
ci.prop.diff

Confidence Interval for the Difference in Proportions
ci.var

(Bootstrap) Confidence Intervals for Variances and Standard Deviations
clear

Clear Console in RStudio
coding

Coding Categorical Variables
ci.mean.diff

Confidence Interval for the Difference in Arithmetic Means
cohens.d

Cohen's d
data.items

Multiple-Choice, Dichotomous, and Polytomous Item Data
descript

Descriptive Statistics
cor.matrix

Correlation Matrix
df.duplicated

Extract Duplicated or Unique Rows
coeff.std

Standardized Coefficients for Linear, Multilevel and Mixed-Effects Models
df.head

Print the First and Last Rows of a Data Frame
coeff.robust

Heteroscedasticity-Consistent and Cluster-Robust Standard Errors
crosstab

Cross Tabulation
df.check

Data Check
df.rename

Rename Columns in a Matrix or Variables in a Data Frame
df.move

Move Variable(s) in a Data Frame
difftest.chibarsq

Chi-Bar-Square Difference Test
df.subset

Subsetting Data Frames
df.rbind

Combine Data Frames by Rows, Filling in Missing Columns
df.sort

Data Frame Sorting
dominance

Dominance Analysis
dominance.manual

Dominance Analysis, Manually Inputting a Correlation Matrix
df.long

Converting Data Frames Between 'Wide' and 'Long' Format
df.merge

Merge Multiple Data Frames