Package: MBESS 4.9.3

MBESS: The MBESS R Package

Implements methods that are useful in designing research studies and analyzing data, with particular emphasis on methods that are developed for or used within the behavioral, educational, and social sciences (broadly defined). That being said, many of the methods implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a suite of functions for a variety of related topics, such as effect sizes, confidence intervals for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and minimum-risk point estimation perspectives), mediation analysis, various properties of distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but MBESS became more general and now contains methods applicable and used in a wide variety of fields and is an orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has greatly benefited from others, see <https://www3.nd.edu/~kkelley/site/MBESS.html> for a detailed list of those that have contributed and other details.

Authors:Ken Kelley [aut, cre]

MBESS_4.9.3.tar.gz
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MBESS.pdf |MBESS.html✨
MBESS/json (API)

# Install 'MBESS' in R:
install.packages('MBESS', repos = c('https://yelleknek.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/yelleknek/mbess/issues

Datasets:
  • Cor.Mat.Lomax - Correlation matrix for Lomax (1983) data set
  • Cor.Mat.MM - Correlation matrix for Maruyama & McGarvey (1980) data set
  • Gardner.LD - The Gardner learning data, which was used by L.R. Tucker
  • HS - Complete Data Set of Holzinger and Swineford's (1939) Study
  • prof.salary - Cohen et. al. (2003)'s professor salary data set

On CRAN:

8.20 score 2 stars 23 packages 258 scripts 8.8k downloads 59 mentions 0 exports 32 dependencies

Last updated 1 years agofrom:3cf5277ac5. Checks:OK: 1 ERROR: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 18 2024
R-4.5-winERROROct 18 2024
R-4.5-linuxERROROct 18 2024
R-4.4-winERROROct 18 2024
R-4.4-macERROROct 18 2024
R-4.3-winERROROct 18 2024
R-4.3-macERROROct 18 2024

Exports:

Dependencies:abindarmBHbootclicodadigestgluelatticelavaanlifecyclelme4MASSMatrixmiminqamnormtmvtnormnlmenloptrnumDerivOpenMxpbivnormquadprogRcppRcppEigenRcppParallelrlangrpfsemsemToolsStanHeaders

Readme and manuals

Help Manual

Help pageTopics
Sample size planning for the standardized mean different from the accuracy in parameter estimation approachss.aipe.smd.full ss.aipe.smd.lower ss.aipe.smd.upper
Generate random data for an ANCOVA modelancova.random.data
One-factor confirmatory factor analysis modelCFA.1
Confidence interval for a contrast in a fixed effects ANOVAci.c
Confidence interval for an (unstandardized) contrast in ANCOVA with one covariateci.c.ancova
Confidence interval for the population correlation coefficientci.cc
Confidence interval for the coefficient of variationci.cv
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.omega2
Confidence Interval for the Proportion of Variance Accounted for (in the dependent variable by knowing the levels of the factor)ci.pvaf
Confidence interval for the multiple correlation coefficientci.R
Confidence interval for the population squared multiple correlation coefficientci.R2
Confidence Interval for a Regression Coefficientci.rc
Confidence interval for a regression coefficientci.reg.coef
Confidence Interval for a Reliability Coefficientci.reliability
Confidence interval for the population root mean square error of approximationci.rmsea
Confidence Interval for a Standardized Contrast in a Fixed Effects ANOVAci.sc
Confidence interval for a standardized contrast in ANCOVA with one covariateci.sc.ancova
Confidence Interval for the Standardized Meanci.sm
Confidence limits for the standardized mean difference.ci.smd
Confidence limits for the standardized mean difference using the control group standard deviation as the divisor.ci.smd.c
Confidence Interval for the Signal-To-Noise Ratioci.snr
Confidence Interval for a Standardized Regression Coefficientci.src
Confidence Interval for the Square Root of the Signal-To-Noise Ratioci.srsnr
Confidence limits for noncentral chi square parametersconf.limits.nc.chisq
Confidence limits for noncentral F parametersconf.limits.ncf
Confidence limits for a noncentrality parameter from a t-distributionconf.limits.nct
Correlation matrix for Lomax (1983) data setCor.Mat.Lomax
Correlation matrix for Maruyama & McGarvey (1980) data setCor.Mat.MM
Correlation Matrix to Covariance Matrix Conversioncor2cov
Covariance matrix from confirmatory (single) factor model.covmat.from.cfm
Function to calculate the regular (which is also biased) estimate of the coefficient of variation or the unbiased estimate of the coefficient of variation.cv
Expected value of the squared multiple correlation coefficientExpected.R2
Conversion functions from noncentral noncentral values to their corresponding and vice versa, for those related to the F-test and R Square.F2Rsquare Lambda2Rsquare Rsquare2F Rsquare2Lambda
The Gardner learning data, which was used by L.R. TuckerGardner.LD
Complete Data Set of Holzinger and Swineford's (1939) StudyHS
Regression Surface Containing Interactionintr.plot
Plotting Conditional Regression Lines with Interactions in Two Dimensionsintr.plot.2d
MBESSMBES mbes MBESS mbess
Effect sizes and confidence intervals in a mediation modelmediation
Bar plots of mediation effectsmediation.effect.bar.plot
Visualizing mediation effectsmediation.effect.plot
Minimum risk point estimation of the population coefficient of variationmr.cv
Minimum risk point estimation of the population standardized mean differencemr.smd
Density for power of two one-sided tests procedure (TOST) for equivalencepower.density.equivalence.md
Power of Two One-Sided Tests Procedure (TOST) for Equivalencepower.equivalence.md
Plot power of Two One-Sided Tests Procedure (TOST) for Equivalencepower.equivalence.md.plot
Cohen et. al. (2003)'s professor salary data setprof.salary
Unbiased estimate of the population standard deviations.u
Construct a covariance matrix with specified error of approximationSigma.2.SigmaStar
Signal to noise using squared multiple correlation coefficientsignal.to.noise.R2
Standardized mean differencesmd
Standardized mean difference using the control group as the basis of standardizationsmd.c
Sample size planning for an ANOVA contrast from the Accuracy in Parameter Estimation (AIPE) perspectivess.aipe.c
Sample size planning for a contrast in randomized ANCOVA from the Accuracy in Parameter Estimation (AIPE) perspectivess.aipe.c.ancova
Sensitivity analysis for sample size planning for the (unstandardized) contrast in randomized ANCOVA from the Accuracy in Parameter Estimation (AIPE) Perspectivess.aipe.c.ancova.sensitivity
Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRDss.aipe.crd.both.fixedbudget ss.aipe.crd.both.fixedwidth ss.aipe.crd.nclus.fixedbudget ss.aipe.crd.nclus.fixedwidth ss.aipe.crd.nindiv.fixedbudget ss.aipe.crd.nindiv.fixedwidth
Find target sample sizes for the accuracy in standardized conditions means estimation in CRDss.aipe.crd.es.both.fixedbudget ss.aipe.crd.es.both.fixedwidth ss.aipe.crd.es.nclus.fixedbudget ss.aipe.crd.es.nclus.fixedwidth ss.aipe.crd.es.nindiv.fixedbudget ss.aipe.crd.es.nindiv.fixedwidth
Sample size planning for the coefficient of variation given the goal of Accuracy in Parameter Estimation approach to sample size planningss.aipe.cv
Sensitivity analysis for sample size planning given the Accuracy in Parameter Estimation approach for the coefficient of variation.ss.aipe.cv.sensitivity
Sample size planning for polynomial change models in longitudinal studyss.aipe.pcm
Sample Size Planning for Accuracy in Parameter Estimation for the multiple correlation coefficient.ss.aipe.R2
Sensitivity analysis for sample size planning with the goal of Accuracy in Parameter Estimation (i.e., a narrow observed confidence interval)ss.aipe.R2.sensitivity
Sample size necessary for the accuracy in parameter estimation approach for an unstandardized regression coefficient of interestss.aipe.rc
Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the unstandardized regression coefficientss.aipe.rc.sensitivity
Sample size necessary for the accuracy in parameter estimation approach for a regression coefficient of interestss.aipe.reg.coef
Sensitivity analysis for sample size planning from the Accuracy in Parameter Estimation Perspective for the (standardized and unstandardized) regression coefficientss.aipe.reg.coef.sensitivity
Sample Size Planning for Accuracy in Parameter Estimation for Reliability Coefficients.ss.aipe.reliability
Sample size planning for RMSEA in SEMss.aipe.rmsea
a priori Monte Carlo simulation for sample size planning for RMSEA in SEMss.aipe.rmsea.sensitivity
Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized contrast in ANOVAss.aipe.sc
Sample size planning from the AIPE perspective for standardized ANCOVA contrastsss.aipe.sc.ancova
Sensitivity analysis for the sample size planning method for standardized ANCOVA contrastss.aipe.sc.ancova.sensitivity
Sensitivity analysis for sample size planning for the standardized ANOVA contrast from the Accuracy in Parameter Estimation (AIPE) Perspectivess.aipe.sc.sensitivity
Sample size planning for SEM targeted effectsss.aipe.sem.path
a priori Monte Carlo simulation for sample size planning for SEM targeted effectsss.aipe.sem.path.sensitiv
Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized meanss.aipe.sm
Sensitivity analysis for sample size planning for the standardized mean from the Accuracy in Parameter Estimation (AIPE) Perspectivess.aipe.sm.sensitivity
Sample size planning for the standardized mean difference from the Accuracy in Parameter Estimation (AIPE) perspectivess.aipe.smd
Sensitivity analysis for sample size given the Accuracy in Parameter Estimation approach for the standardized mean difference.ss.aipe.smd.sensitivity
sample size necessary for the accuracy in parameter estimation approach for a standardized regression coefficient of interestss.aipe.src
Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the standardized regression coefficientss.aipe.src.sensitivity
Sample size planning for power for polynomial change modelsss.power.pcm
Function to plan sample size so that the test of the squared multiple correlation coefficient is sufficiently powerful.ss.power.R2
sample size for a targeted regression coefficientss.power.rc
sample size for a targeted regression coefficientss.power.reg.coef
Sample size planning for structural equation modeling from the power analysis perspectivess.power.sem
Conversion functions for noncentral t-distributiondelta2lambda lambda2delta
Compute the model-implied covariance matrix of an SEM modeltheta.2.Sigma.theta
Transform a correlation coefficient (r) into the scale of Fischer's _Z_transform_r.Z
Transform Fischer's _Z_ into the scale of a correlation coefficienttransform_Z.r
This function implements the upsilon effect size statistic as described in Lachowicz, Preacher, & Kelley (in press) for mediation.upsilon
The Variance of the Estimated Treatment Effect at Selected Covariate Values in a Two-group ANCOVA.var.ete
Variance of squared multiple correlation coefficientVariance.R2
Internal MBESS function for verifying the sample size in ss.aipe.R2verify.ss.aipe.R2
Visualize individual trajectoriesvit
Visualize individual trajectories with fitted curve and quality of fitvit.fitted