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MBESS

The MBESS R Package (Now on GitHub)

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Install

install.packages('MBESS')

Monthly Downloads

11,414

Version

4.9.42

License

GPL-2 | GPL-3

Maintainer

Ken Kelley

Last Published

January 8th, 2026

Functions in MBESS (4.9.42)

ci.pvaf

Confidence Interval for the Proportion of Variance Accounted for (in the dependent variable by knowing the levels of the factor)
ci.c

Confidence interval for a contrast in a fixed effects ANOVA
ci.cc

Confidence interval for the population correlation coefficient
ci.omega2

Confidence Interval for omega-squared (\(\omega^2\)) for between-subject fixed-effects ANOVA and ANCOVA designs (and partial omega-squared \(\omega^2_p\) for between-subject multifactor ANOVA and ANCOVA designs)
ci.c.ancova

Confidence interval for an (unstandardized) contrast in ANCOVA with one covariate
ci.R2

Confidence interval for the population squared multiple correlation coefficient
aipe.smd

Sample size planning for the standardized mean different from the accuracy in parameter estimation approach
ci.R

Confidence interval for the multiple correlation coefficient
ci.cv

Confidence interval for the coefficient of variation
ancova.random.data

Generate random data for an ANCOVA model
ci.sm

Confidence Interval for the Standardized Mean
ci.snr

Confidence Interval for the Signal-To-Noise Ratio
ci.sc

Confidence Interval for a Standardized Contrast in a Fixed Effects ANOVA
ci.smd.c

Confidence limits for the standardized mean difference using the control group standard deviation as the divisor.
ci.smd

Confidence limits for the standardized mean difference.
ci.reliability

Confidence Interval for a Reliability Coefficient
ci.sc.ancova

Confidence interval for a standardized contrast in ANCOVA with one covariate
ci.rc

Confidence Interval for a Regression Coefficient
ci.reg.coef

Confidence interval for a regression coefficient
ci.srsnr

Confidence Interval for the Square Root of the Signal-To-Noise Ratio
ci.src

Confidence Interval for a Standardized Regression Coefficient
conf.limits.nc.chisq

Confidence limits for noncentral chi square parameters
cv

Function to calculate the regular (which is also biased) estimate of the coefficient of variation or the unbiased estimate of the coefficient of variation.
covmat.from.cfm

Covariance matrix from confirmatory (single) factor model.
cor2cov

Correlation Matrix to Covariance Matrix Conversion
ci.rmsea

Confidence interval for the population root mean square error of approximation
conf.limits.ncf

Confidence limits for noncentral F parameters
conf.limits.nct

Confidence limits for a noncentrality parameter from a t-distribution
intr.plot.2d

Plotting Conditional Regression Lines with Interactions in Two Dimensions
intr.plot

Regression Surface Containing Interaction
mediation

Effect sizes and confidence intervals in a mediation model
mediation.effect.bar.plot

Bar plots of mediation effects
prof.salary

Cohen et. al. (2003)'s professor salary data set
mediation.effect.plot

Visualizing mediation effects
s.u

Unbiased estimate of the population standard deviation
mr.smd

Minimum risk point estimation of the population standardized mean difference
power.density.equivalence.md

Density for power of two one-sided tests procedure (TOST) for equivalence
mr.cv

Minimum risk point estimation of the population coefficient of variation
power.equivalence.md.plot

Plot power of Two One-Sided Tests Procedure (TOST) for Equivalence
power.equivalence.md

Power of Two One-Sided Tests Procedure (TOST) for Equivalence
ss.aipe.R2

Sample Size Planning for Accuracy in Parameter Estimation for the multiple correlation coefficient.
signal.to.noise.R2

Signal to noise using squared multiple correlation coefficient
smd

Standardized mean difference
smd.c

Standardized mean difference using the control group as the basis of standardization
ss.aipe.c.ancova.sensitivity

Sensitivity analysis for sample size planning for the (unstandardized) contrast in randomized ANCOVA from the Accuracy in Parameter Estimation (AIPE) Perspective
ss.aipe.c.ancova

Sample size planning for a contrast in randomized ANCOVA from the Accuracy in Parameter Estimation (AIPE) perspective
ss.aipe.cv.sensitivity

Sensitivity analysis for sample size planning given the Accuracy in Parameter Estimation approach for the coefficient of variation.
ss.aipe.cv

Sample size planning for the coefficient of variation given the goal of Accuracy in Parameter Estimation approach to sample size planning
ss.aipe.rmsea

Sample size planning for RMSEA in SEM
ss.aipe.reg.coef.sensitivity

Sensitivity analysis for sample size planning from the Accuracy in Parameter Estimation Perspective for the (standardized and unstandardized) regression coefficient
ss.aipe.c

Sample size planning for an ANOVA contrast from the Accuracy in Parameter Estimation (AIPE) perspective
ss.aipe.R2.sensitivity

Sensitivity analysis for sample size planning with the goal of Accuracy in Parameter Estimation (i.e., a narrow observed confidence interval)
ss.aipe.sc

Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized contrast in ANOVA
ss.aipe.sc.ancova

Sample size planning from the AIPE perspective for standardized ANCOVA contrasts
ss.aipe.rmsea.sensitivity

a priori Monte Carlo simulation for sample size planning for RMSEA in SEM
ss.aipe.reliability

Sample Size Planning for Accuracy in Parameter Estimation for Reliability Coefficients.
ss.aipe.rc.sensitivity

Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the unstandardized regression coefficient
ss.aipe.reg.coef

Sample size necessary for the accuracy in parameter estimation approach for a regression coefficient of interest
ss.aipe.pcm

Sample size planning for polynomial change models in longitudinal study
ss.aipe.sc.ancova.sensitivity

Sensitivity analysis for the sample size planning method for standardized ANCOVA contrast
ss.aipe.src

sample size necessary for the accuracy in parameter estimation approach for a standardized regression coefficient of interest
ss.aipe.sm

Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized mean
ss.aipe.rc

Sample size necessary for the accuracy in parameter estimation approach for an unstandardized regression coefficient of interest
ss.aipe.src.sensitivity

Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the standardized regression coefficient
ss.aipe.smd.sensitivity

Sensitivity analysis for sample size given the Accuracy in Parameter Estimation approach for the standardized mean difference.
ss.aipe.smd

Sample size planning for the standardized mean difference from the Accuracy in Parameter Estimation (AIPE) perspective
ss.aipe.sm.sensitivity

Sensitivity analysis for sample size planning for the standardized mean from the Accuracy in Parameter Estimation (AIPE) Perspective
ss.aipe.sc.sensitivity

Sensitivity analysis for sample size planning for the standardized ANOVA contrast from the Accuracy in Parameter Estimation (AIPE) Perspective
ss.power.sem

Sample size planning for structural equation modeling from the power analysis perspective
theta.2.Sigma.theta

Compute the model-implied covariance matrix of an SEM model
ss.power.rc

sample size for a targeted regression coefficient
transform_Z.r

Transform Fischer's Z into the scale of a correlation coefficient
ss.aipe.crd

Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRD
t.and.smd.conversion

Conversion functions for noncentral t-distribution
ss.power.R2

Function to plan sample size so that the test of the squared multiple correlation coefficient is sufficiently powerful.
ss.aipe.crd.es

Find target sample sizes for the accuracy in standardized conditions means estimation in CRD
ss.aipe.sem.path.sensitiv

a priori Monte Carlo simulation for sample size planning for SEM targeted effects
ss.aipe.sem.path

Sample size planning for SEM targeted effects
transform_r.Z

Transform a correlation coefficient (r) into the scale of Fisher's \(Z^\prime\)
verify.ss.aipe.R2

Internal MBESS function for verifying the sample size in ss.aipe.R2
ss.power.reg.coef

sample size for a targeted regression coefficient
ss.power.pcm

Sample size planning for power for polynomial change models
upsilon

This function implements the upsilon effect size statistic as described in Lachowicz, Preacher, & Kelley (2018) for mediation.
var.ete

The Variance of the Estimated Treatment Effect at Selected Covariate Values in a Two-group ANCOVA.
vit.fitted

Visualize individual trajectories with fitted curve and quality of fit
vit

Visualize individual trajectories
Gardner.LD

The Gardner learning data, which was used by L.R. Tucker
Sigma.2.SigmaStar

Construct a covariance matrix with specified error of approximation
MBESS

MBESS
HS

Complete Data Set of Holzinger and Swineford's (1939) Study
CFA.1

One-factor confirmatory factor analysis model
Cor.Mat.Lomax

Correlation matrix for Lomax (1983) data set
F.and.R2.Noncentral.Conversion

Conversion functions from noncentral noncentral values to their corresponding and vice versa, for those related to the F-test and R Square.
Variance.R2

Variance of squared multiple correlation coefficient
Cor.Mat.MM

Correlation matrix for Maruyama & McGarvey (1980) data set
Expected.R2

Expected value of the squared multiple correlation coefficient