Correlation Matrices from Boer et al. (2016)
Fifty Studies of Correlation Matrices used in Cheung and Chan (2000)
Six Studies of Correlation Matrices reported by Becker (1992; 1995)
Ten Studies of Correlation Matrices used by Becker (2009)
A Dataset from Bornmann et al. (2007)
Multivariate effect sizes between classroom management
self-efficacy (CMSE) and other variables reported by Aloe et al. (2014)
Five Published Trails from Berkey et al. (1998)
Five Studies of Ten Correlation Matrices reported by Becker and
Schram (1994)
Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis
Studies on Sex Differences in Conformity Reported by Becker (1983)
Selected effect sizes from Cooper et al. (2003)
Correlation coefficients reported by Tenenbaum and Leaper (2002)
Fourteen Studies of Correlation Matrices reported by Hunter (1983)
Extract Variance-Covariance Matrix of the Random Effects
Dataset from Jaramillo, Mulki & Marshall (2005)
Simulated Effect Sizes Reported by Hox (2002)
Effects of Open Education Reported by Hedges and Olkin (1985)
Eight studies from Mak et al. (2009)
Correlation Matrices from Cooke et al. (2016)
A Dataset from TSSEM User's Guide Version 1.11 by Cheung (2009)
Two Datasets from Gleser and Olkin (1994)
Multivariate effect sizes reported by Kalaian and Raudenbush (1996)
Check the correctness of the RAM formulation
Test Positive Definiteness of a List of Square Matrices
Summary Method for tssem1, wls, meta, and meta3X Objects
Fit Models on the bootstrapped correlation matrices
A Dataset from ISSP (2005)
Dataset on the Environmental Tobacco Smoke (ETS) on children's health
Convert correlation or covariance matrices into a dataframe of correlations or
covariances with their sampling covariance matrices
Correlation Matrices from Gnambs, Scharl, and Schroeders (2018)
Convert a Matrix into MxMatrix-class
Matrix Diagonals
Studies on Students' School Engagement and Achievement Reported
by Roorda et al. (2011)
Correlation Matrices from Nohe et al. (2015)
Studies on the Hospital Anxiety and Depression Scale Reported by Norton et al. (2013)
Convert a Character Matrix with Starting Values to a Character Matrix
without Starting Values
Compare Nested Models with Likelihood Ratio Statistic
Convert a List of Symmetric Matrices into a Stacked Matrix
Convert a Matrix into a Block Diagonal Matrix
Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix
First Stage of the Two-Stage Structural Equation Modeling (TSSEM)
Extract Parameter Estimates from various classes.
Create an F matrix to select observed variables
Display the Pattern of Missing Data of a List of Square Matrices
Forty-four Studies from Cheung (2013)
Convert a Character Matrix into MxAlgebra-class
Conduct a Correlation/Covariance Structure Analysis with WLS
Create a Block Diagonal Matrix
Create a Vector into MxMatrix-class
Create a model implied correlation matrix with implicit diagonal constraints
Create or Generate the Model Implied Correlation or Covariance Matrices
Plot methods for various objects
Estimate the asymptotic covariance matrix of standardized or unstandardized indirect and direct effects
A Dataset from Cheung and Chan (2005; 2009)
Compute Effect Sizes for Multiple Treatment Studies
Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation
Factor Correlation Matrices of Big Five Model from Digman (1997)
Compute Effect Sizes for Multiple End-point Studies
Read External Correlation/Covariance Matrices
Create an mxModel
Estimate Variance Components with Restricted (Residual) Maximum
Likelihood Estimation
Meta-Analysis using Structural Equation Modeling
Test the Homogeneity of Effect Sizes
Forty-four Covariance Matrices on Life Satisfaction, Job Satisfaction, and Job Autonomy
Convert lavaan
models to RAM models
Correlations from Stadler et al. (2015)
Create a variance component of the heterogeneity of the random effects
Correlation Matrices from Scalco et al. (2017)
Create a Block Diagonal Matrix by Repeating the Input
Display the Accumulative Sample Sizes for the Covariance Matrix
Calculate the SRMR in OSMASEM
Univariate and Multivariate Meta-Analysis with Maximum
Likelihood Estimation
Parametric bootstrap on the univariate R (uniR) object
Create a V-known matrix
Estimate Variance Components in Three-Level Univariate
Meta-Analysis with Restricted (Residual) Maximum
Likelihood Estimation
One-stage meta-analytic structural equation modeling
Convert metaSEM
objects into semPlotModel
objects for plotting
Print Methods for various Objects
Rerun models via mxTryHard()
Second Stage analysis of the univariate R (uniR) approach
Generate Sample/Population Correlation/Covariance Matrices
Calculate the R2 in OSMASEM
First Stage analysis of the univariate R (uniR) approach
Convert a Vector into a Symmetric Matrix
Extract Covariance Matrix Parameter Estimates from Objects of
Various Classes
Estimate the heterogeneity (SD) of the parameter estimates of the
TSSEM object
Dataset on the effectiveness of multidimensional family therapy in treating
adolescents with multiple behavior problems