# MBESS v4.8.0

Monthly downloads

## 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.

## Readme

# MBESS

The MBESS R Package (Now on GitHub)

## Functions in MBESS

Name | Description | |

HS | Complete Data Set of Holzinger and Swineford's (1939) Study | |

MBESS | MBESS | |

ci.cc | Confidence interval for the population 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. | |

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

Cor.Mat.Lomax | Correlation matrix for Lomax (1983) data set | |

CFA.1 | One-factor confirmatory factor analysis model | |

ancova.random.data | Generate random data for an ANCOVA model | |

aipe.smd | Sample size planning for the standardized mean different from the accuracy in parameter estimation approach | |

ci.c | Confidence interval for a contrast in a fixed effects ANOVA | |

ci.c.ancova | Confidence interval for an (unstandardized) contrast in ANCOVA with one covariate | |

Sigma.2.SigmaStar | Construct a covariance matrix with specified error of approximation | |

ci.sm | Confidence Interval for the Standardized Mean | |

ci.rmsea | Confidence interval for the population root mean square error of approximation | |

ci.sc.ancova | Confidence interval for a standardized contrast in ANCOVA with one covariate | |

Variance.R2 | Variance of squared multiple correlation coefficient | |

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

ci.reg.coef | Confidence interval for a regression coefficient | |

ci.cv | Confidence interval for the coefficient of variation | |

ci.smd | Confidence limits for the standardized mean difference. | |

intr.plot.2d | Plotting Conditional Regression Lines with Interactions in Two Dimensions | |

ci.sc | Confidence Interval for a Standardized Contrast in a Fixed Effects ANOVA | |

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. | |

intr.plot | Regression Surface Containing Interaction | |

mediation | Effect sizes and confidence intervals in a mediation model | |

conf.limits.ncf | Confidence limits for noncentral F parameters | |

conf.limits.nct | Confidence limits for a noncentrality parameter from a t-distribution | |

ci.src | Confidence Interval for a Standardized Regression Coefficient | |

ci.rc | Confidence Interval for a Regression Coefficient | |

ci.snr | Confidence Interval for the Signal-To-Noise Ratio | |

ci.reliability | Confidence Interval for a Reliability Coefficient | |

cor2cov | Correlation Matrix to Covariance Matrix Conversion | |

mr.cv | Minimum risk point estimation of the population coefficient of variation | |

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

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

covmat.from.cfm | Covariance matrix from confirmatory (single) factor model. | |

power.density.equivalence.md | Density for power of two one-sided tests procedure (TOST) for equivalence | |

mr.smd | Minimum risk point estimation of the population standardized mean difference | |

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

s.u | Unbiased estimate of the population standard deviation | |

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

signal.to.noise.R2 | Signal to noise using squared multiple correlation coefficient | |

ci.R | Confidence interval for the multiple correlation coefficient | |

Cor.Mat.MM | Correlation matrix for Maruyama & McGarvey (1980) data set | |

Expected.R2 | Expected value of the squared multiple correlation coefficient | |

ss.aipe.R2 | Sample Size Planning for Accuracy in Parameter Estimation for the multiple correlation coefficient. | |

ci.R2 | Confidence interval for the population squared multiple correlation coefficient | |

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) | |

prof.salary | Cohen et. al. (2003)'s professor salary data set | |

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 | |

smd | Standardized mean difference | |

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.reg.coef | Sample size necessary for the accuracy in parameter estimation approach for a regression coefficient of interest | |

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 | |

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

ss.aipe.reliability | Sample Size Planning for Accuracy in Parameter Estimation for Reliability Coefficients. | |

conf.limits.nc.chisq | Confidence limits for noncentral chi square parameters | |

ss.aipe.rmsea | Sample size planning for RMSEA in SEM | |

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

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

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

ss.aipe.sc.ancova | Sample size planning from the AIPE perspective for standardized ANCOVA contrasts | |

ss.power.reg.coef | sample size for a targeted regression coefficient | |

ss.power.sem | Sample size planning for structural equation modeling from the power analysis perspective | |

ss.aipe.sc.ancova.sensitivity | Sensitivity analysis for the sample size planning method for standardized ANCOVA contrast | |

ss.aipe.sem.path | Sample size planning for SEM targeted effects | |

t.and.smd.conversion | Conversion functions for noncentral t-distribution | |

ss.power.pcm | Sample size planning for power for polynomial change models | |

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

smd.c | Standardized mean difference using the control group as the basis of standardization | |

ss.power.R2 | Function to plan sample size so that the test of the squared multiple correlation coefficient is sufficiently powerful. | |

theta.2.Sigma.theta | Compute the model-implied covariance matrix of an SEM model | |

mediation.effect.bar.plot | Bar plots of mediation effects | |

mediation.effect.plot | Visualizing mediation effects | |

ss.aipe.rmsea.sensitivity | a priori Monte Carlo simulation for sample size planning for RMSEA in SEM | |

vit.fitted | Visualize individual trajectories with fitted curve and quality of fit | |

ss.power.rc | sample size for a targeted regression coefficient | |

verify.ss.aipe.R2 | Internal MBESS function for verifying the sample size in ss.aipe.R2 | |

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

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

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

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

ss.aipe.pcm | Sample size planning for polynomial change models in longitudinal study | |

vit | Visualize individual trajectories | |

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

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 | |

ss.aipe.rc.sensitivity | Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the unstandardized regression coefficient | |

var.ete | The Variance of the Estimated Treatment Effect at Selected Covariate Values in a Two-group ANCOVA. | |

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

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

transform_Z.r | Transform Fischer's Z into the scale of a correlation coefficient | |

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

transform_r.Z | Transform a correlation coefficient (r) into the scale of Fischer's Z | |

No Results! |

## Last month downloads

## Details

Type | Package |

Date | 2020-08-04 |

License | GPL-2 | GPL-3 |

URL | http://nd.edu/~kkelley/site/MBESS.html |

RoxygenNote | 6.0.0 |

NeedsCompilation | no |

Packaged | 2020-08-04 19:07:45 UTC; kkelley |

Repository | CRAN |

Date/Publication | 2020-08-05 04:50:03 UTC |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/MBESS)](http://www.rdocumentation.org/packages/MBESS)
```