metaSEM

Multivariate effect sizes between classroom management self-efficacy (CMSE) and other variables reported by Aloe et al. (2014)

Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis

Ten Studies of Correlation Matrices used by Becker (2009)

Studies on Sex Differences in Conformity Reported by Becker (1983)

Six Studies of Correlation Matrices reported by Becker (1992; 1995)

Five Studies of Ten Correlation Matrices reported by Becker and Schram (1994)

Five Published Trails from Berkey et al. (1998)

Correlation Matrices from Boer et al. (2016)

A Dataset from Bornmann et al. (2007)

Fifty Studies of Correlation Matrices used in Cheung and Chan (2000)

A Dataset from TSSEM User's Guide Version 1.11 by Cheung (2009)

Correlation Matrices from Cooke et al. (2016)

Selected effect sizes from Cooper et al. (2003)

Convert correlation or covariance matrices into a dataframe of correlations or covariances with their sampling covariance matrices

Matrix Diagonals

Factor Correlation Matrices of Big Five Model from Digman (1997)

Two Datasets from Gleser and Olkin (1994)

Correlation Matrices from Gnambs, Scharl, and Schroeders (2018)

Effects of Open Education Reported by Hedges and Olkin (1985)

Simulated Effect Sizes Reported by Hox (2002)

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Dataset from Jaramillo, Mulki & Marshall (2005)

Multivariate effect sizes reported by Kalaian and Raudenbush (1996)

Eight studies from Mak et al. (2009)

Correlation Matrices from Mathieu et al. (2015)

Dataset on the Environmental Tobacco Smoke (ETS) on children's health

Correlation Matrices from Nohe et al. (2015)

Studies on the Hospital Anxiety and Depression Scale Reported by Norton et al. (2013)

Studies on Students' School Engagement and Achievement Reported by Roorda et al. (2011)

Correlation Matrices from Scalco et al. (2017)

Correlations from Stadler et al. (2015)

Correlation coefficients reported by Tenenbaum and Leaper (2002)

Extract Variance-Covariance Matrix of the Random Effects

Compare Nested Models with Likelihood Ratio Statistic

Convert a Character Matrix into MxAlgebra-class

Convert a Matrix into MxMatrix-class

Convert a Character Matrix with Starting Values to a Character Matrix without Starting Values

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Create a Block Diagonal Matrix

Create a Block Diagonal Matrix by Repeating the Input

Parametric bootstrap on the univariate R (uniR) object

Fit Models on the bootstrapped correlation matrices

Calculate Effect Sizes using lavaan Models

Check the correctness of the RAM formulation

Extract Parameter Estimates from various classes.

Create an F matrix to select observed variables

Create a variance component of the heterogeneity of the random effects

Create a V-known matrix

Create a moderator matrix used in OSMASEM

Create a Vector into MxMatrix-class

Create an mxModel

Create a model implied correlation matrix with implicit diagonal constraints

Test the Homogeneity of Effect Sizes

Create or Generate the Model Implied Correlation or Covariance Matrices

Estimate the asymptotic covariance matrix of standardized or unstandardized indirect and direct effects

Test Positive Definiteness of a List of Square Matrices

A Dataset from ISSP (2005)

A Dataset from Cheung and Chan (2005; 2009)

Convert lavaan models to RAM models

Convert a List of Symmetric Matrices into a Stacked Matrix

Convert a Matrix into a Block Diagonal Matrix

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

Convert metaSEM objects into semPlotModel objects for plotting

Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation

Meta-Analysis using Structural Equation Modeling

One-stage meta-analytic structural equation modeling

Calculate the R2 in OSMASEM

Calculate the SRMR in OSMASEM

Display the Accumulative Sample Sizes for the Covariance Matrix

Display the Pattern of Missing Data of a List of Square Matrices

Plot methods for various objects

Print Methods for various Objects

Generate Sample/Population Correlation/Covariance Matrices

Read External Correlation/Covariance Matrices

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

Estimate Variance Components in Three-Level Univariate Meta-Analysis with Restricted (Residual) Maximum Likelihood Estimation

Rerun models via mxTryHard()

Compute Effect Sizes for Multiple End-point Studies

Compute Effect Sizes for Multiple Treatment Studies

Summary Method for tssem1, wls, meta, and meta3X Objects

First Stage of the Two-Stage Structural Equation Modeling (TSSEM)

Estimate the heterogeneity (SD) of the parameter estimates of the TSSEM object

First Stage analysis of the univariate R (uniR) approach

Second Stage analysis of the univariate R (uniR) approach

Dataset on the effectiveness of multidimensional family therapy in treating adolescents with multiple behavior problems

Extract Covariance Matrix Parameter Estimates from Objects of Various Classes

Convert a Vector into a Symmetric Matrix

Conduct a Correlation/Covariance Structure Analysis with WLS

Forty-four Studies from Cheung (2013)

Forty-four Covariance Matrices on Life Satisfaction, Job Satisfaction, and Job Autonomy