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The MBESS R Package

Implements methods that 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 at this point MBESS 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 <http://nd.edu/~kkelley/site/MBESS.html> for a detailed list of those that have contributed and other details.

Functions in MBESS

Name Description
aipe.smd Sample size planning for the standardized mean different from the accuracy in parameter estimation approach
ci.cv Confidence interval for the coefficient of variation
ci.src Confidence Interval for a Standardized Regression Coefficient
ci.cc Confidence interval for the population correlation coefficient
ci.snr Confidence Interval for the Signal-To-Noise Ratio
ci.sc.ancova Confidence interval for a standardized contrast in ANCOVA with one covariate
ancova.random.data Generate random data for an ANCOVA model
ci.sm Confidence Interval for the Standardized Mean
HS.data Complete Data Set of Holzinger and Swineford's (1939) Study
ci.reg.coef Confidence interval for a regression coefficient
MBESS MBESS
conf.limits.nct Confidence limits for a noncentrality parameter from a t-distribution
conf.limits.ncf Confidence limits for noncentral F parameters
ci.c Confidence interval for a contrast in a fixed effects ANOVA
ci.pvaf Confidence Interval for the Proportion of Variance Accounted for (in the dependent variable by knowing the levels of the factor)
ci.c.ancova Confidence interval for an (unstandardized) contrast in ANCOVA with one covariate
covmat.from.cfm Covariance matrix from confirmatory (single) factor model.
smd.c Standardized mean difference using the control group as the basis of standardization
signal.to.noise.R2 Signal to noise using squared multiple correlation coefficient
cor2cov Correlation Matrix to Covariance Matrix Conversion
s.u Unbiased estimate of the population standard deviation
smd Standardized mean difference
ci.rc Confidence Interval for a Regression Coefficient
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
ss.aipe.smd.sensitivity Sensitivity analysis for sample size given the Accuracy in Parameter Estimation approach for the standardized mean difference.
ci.srsnr Confidence Interval for the Square Root of the Signal-To-Noise Ratio
mr.cv Minimum risk point estimation of the population coefficient of variation
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.rmsea Sample size planning for RMSEA in SEM
mediation.effect.plot Visualizing mediation effects
ss.aipe.reliability Sample Size Planning for Accuracy in Parameter Estimation for Reliability Coefficients.
ss.aipe.R2 Sample Size Planning for Accuracy in Parameter Estimation for the multiple correlation coefficient.
mediation.effect.bar.plot Bar plots of mediation effects
ss.aipe.src sample size necessary for the accuracy in parameter estimation approach for a standardized regression coefficient of interest
ss.aipe.crd Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRD
ss.aipe.crd.es Find target sample sizes for the accuracy in standardized conditions means estimation in CRD
ci.rmsea Confidence interval for the population root mean square error of approximation
ss.aipe.src.sensitivity Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the standardized regression coefficient
ss.power.R2 Function to plan sample size so that the test of the squared multiple correlation coefficient is sufficiently powerful.
ss.aipe.rmsea.sensitivity a priori Monte Carlo simulation for sample size planning for RMSEA in SEM
power.density.equivalence.md Density for power of two one-sided tests procedure (TOST) for equivalence
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
conf.limits.nc.chisq Confidence limits for noncentral chi square parameters
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.sc Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized contrast in ANOVA
power.equivalence.md Power of Two One-Sided Tests Procedure (TOST) for Equivalence
mr.smd Minimum risk point estimation of the population standardized mean difference
ss.aipe.cv.sensitivity Sensitivity analysis for sample size planning given the Accuracy in Parameter Estimation approach for the coefficient of variation.
ci.sc Confidence Interval for a Standardized Contrast in a Fixed Effects ANOVA
intr.plot.2d Plotting Conditional Regression Lines with Interactions in Two Dimensions
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.
ss.aipe.pcm Sample size planning for polynomial change models in longitudinal study
ss.aipe.sem.path.sensitiv a priori Monte Carlo simulation for sample size planning for SEM targeted effects
ss.aipe.rc Sample size necessary for the accuracy in parameter estimation approach for an unstandardized regression coefficient of interest
power.equivalence.md.plot Plot power of Two One-Sided Tests Procedure (TOST) for Equivalence
prof.salary Cohen et. al. (2003)'s professor salary data set
ss.aipe.reg.coef Sample size necessary for the accuracy in parameter estimation approach for a regression coefficient of interest
intr.plot Regression Surface Containing Interaction
ci.R2 Confidence interval for the population squared multiple correlation coefficient
vit.fitted Visualize individual trajectories with fitted curve and quality of fit
ss.aipe.sm.sensitivity Sensitivity analysis for sample size planning for the standardized mean from the Accuracy in Parameter Estimation (AIPE) Perspective
transform_Z.r Transform Fischer's Z into the scale of a correlation coefficient
ss.aipe.rc.sensitivity Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the unstandardized regression coefficient
transform_r.Z Transform a correlation coefficient (r) into the scale of Fischer's Z
ss.aipe.smd Sample size planning for the standardized mean difference from the Accuracy in Parameter Estimation (AIPE) perspective
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
mediation Effect sizes and confidence intervals in a mediation model
ss.aipe.sm Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized mean
ss.aipe.c.ancova Sample size planning for a contrast in randomized ANCOVA from the Accuracy in Parameter Estimation (AIPE) perspective
ss.aipe.sem.path Sample size planning for SEM targeted effects
ss.aipe.sc.ancova.sensitivity Sensitivity analysis for the sample size planning method for standardized ANCOVA contrast
ss.power.reg.coef sample size for a targeted regression coefficient
ss.aipe.sc.sensitivity Sensitivity analysis for sample size planning for the standardized ANOVA contrast from the Accuracy in Parameter Estimation (AIPE) Perspective
ss.aipe.sc.ancova Sample size planning from the AIPE perspective for standardized ANCOVA contrasts
ss.aipe.c Sample size planning for an 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
t.and.smd.conversion Conversion functions for noncentral t-distribution
theta.2.Sigma.theta Compute the model-implied covariance matrix of an SEM model
verify.ss.aipe.R2 Internal MBESS function for verifying the sample size in ss.aipe.R2
vit Visualize individual trajectories
ss.power.pcm Sample size planning for power for polynomial change models
ss.power.rc sample size for a targeted regression coefficient
var.ete The Variance of the Estimated Treatment Effect at Selected Covariate Values in a Two-group ANCOVA.
upsilon This function implements the upsilon effect size statistic as described in Lachowicz, Preacher, & Kelley (in press) for mediation.
Sigma.2.SigmaStar Construct a covariance matrix with specified error of approximation
ci.R Confidence interval for the multiple correlation coefficient
Variance.R2 Variance of squared multiple correlation coefficient
Cor.Mat.Lomax Correlation matrix for Lomax (1983) data set
CFA.1 One-factor confirmatory factor analysis model
Cor.Mat.MM Correlation matrix for Maruyama & McGarvey (1980) data set
Expected.R2 Expected value of the squared multiple correlation coefficient
Gardner.LD The Gardner learning data, which was used by L.R. Tucker
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.
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Details

Type Package
Date 2019-6-12
License GPL-2 | GPL-3
URL http://nd.edu/~kkelley/site/MBESS.html
RoxygenNote 6.0.0
NeedsCompilation no
Packaged 2019-06-12 19:59:57 UTC; kkelley
Repository CRAN
Date/Publication 2019-06-12 20:30:03 UTC

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