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