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