A Dataset from Bornmann et al. (2007)
Five Studies of Ten Correlation Matrices reported by Becker and
Schram (1994)
Correlation Matrices from Boer et al. (2016)
Six Studies of Correlation Matrices reported by Becker (1992; 1995)
Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis
Five Published Trails from Berkey et al. (1998)
Studies on Sex Differences in Conformity Reported by Becker (1983)
Multivariate effect sizes between classroom management
self-efficacy (CMSE) and other variables reported by Aloe et al. (2014)
Ten Studies of Correlation Matrices used by Becker (2009)
Fifty Studies of Correlation Matrices used in Cheung and Chan (2000)
Convert correlation or covariance matrices into a dataframe of correlations or
covariances with their sampling covariance matrices
Matrix Diagonals
Two Datasets from Gleser and Olkin (1994)
Factor Correlation Matrices of Big Five Model from Digman (1997)
Simulated Effect Sizes Reported by Hox (2002)
A Dataset from TSSEM User's Guide Version 1.11 by Cheung (2009)
Selected effect sizes from Cooper et al. (2003)
Correlation Matrices from Gnambs, Scharl, and Schroeders (2018)
Correlation Matrices from Cooke et al. (2016)
Effects of Open Education Reported by Hedges and Olkin (1985)
Studies on Students' School Engagement and Achievement Reported
by Roorda et al. (2011)
Eight studies from Mak et al. (2009)
Fourteen Studies of Correlation Matrices reported by Hunter (1983)
Correlation Matrices from Nohe et al. (2015)
Multivariate effect sizes reported by Kalaian and Raudenbush (1996)
Correlation Matrices from Mathieu et al. (2015)
Studies on the Hospital Anxiety and Depression Scale Reported by Norton et al. (2013)
Dataset from Jaramillo, Mulki & Marshall (2005)
Dataset on the Environmental Tobacco Smoke (ETS) on children's health
Extract Variance-Covariance Matrix of the Random Effects
Create a Block Diagonal Matrix
Convert a Matrix into MxMatrix-class
Compare Nested Models with Likelihood Ratio Statistic
Correlation coefficients reported by Tenenbaum and Leaper (2002)
Convert a Character Matrix with Starting Values to a Character Matrix
without Starting Values
Create a Block Diagonal Matrix by Repeating the Input
Correlations from Stadler et al. (2015)
Correlation Matrices from Scalco et al. (2017)
Convert a Character Matrix into MxAlgebra-class
Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix
Fit Models on the bootstrapped correlation matrices
Create an F matrix to select observed variables
Extract Parameter Estimates from various classes.
Calculate Effect Sizes using lavaan Models
Create a Vector into MxMatrix-class
Parametric bootstrap on the univariate R (uniR) object
Create a moderator matrix used in OSMASEM
Create a V-known matrix
Estimate the asymptotic covariance matrix of standardized or unstandardized indirect and direct effects
Create a variance component of the heterogeneity of the random effects
Test Positive Definiteness of a List of Square Matrices
Check the correctness of the RAM formulation
Test the Homogeneity of Effect Sizes
A Dataset from Cheung and Chan (2005; 2009)
Create an mxModel
A Dataset from ISSP (2005)
Create or Generate the Model Implied Correlation or Covariance Matrices
Create a model implied correlation matrix with implicit diagonal constraints
Convert a List of Symmetric Matrices into a Stacked Matrix
Convert lavaan
models to RAM models
Convert a Matrix into a Block Diagonal Matrix
Calculate the R2 in OSMASEM and OSMASEM3L
Calculate the SRMR in OSMASEM and OSMASEM3L
Univariate and Multivariate Meta-Analysis with Maximum
Likelihood Estimation
Display the Pattern of Missing Data of a List of Square Matrices
Display the Accumulative Sample Sizes for the Covariance Matrix
Meta-Analysis using Structural Equation Modeling
One-stage meta-analytic structural equation modeling
Convert metaSEM
objects into semPlotModel
objects for plotting
Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation
Estimate Variance Components with Restricted (Residual) Maximum
Likelihood Estimation
Estimate Variance Components in Three-Level Univariate
Meta-Analysis with Restricted (Residual) Maximum
Likelihood Estimation
Generate (Nested) Sample/Population Correlation/Covariance Matrices
Compute Effect Sizes for Multiple Treatment Studies
Read External Correlation/Covariance Matrices
Compute Effect Sizes for Multiple End-point Studies
Plot methods for various objects
Summary Method for tssem1, wls, meta, and meta3LFIML Objects
Rerun models via mxTryHard()
Print Methods for various Objects
Dataset on the effectiveness of multidimensional family therapy in treating
adolescents with multiple behavior problems
Forty-four Studies from Cheung (2013)
Second Stage analysis of the univariate R (uniR) approach
Extract Covariance Matrix Parameter Estimates from Objects of
Various Classes
Forty-four Covariance Matrices on Life Satisfaction, Job Satisfaction, and Job Autonomy
First Stage of the Two-Stage Structural Equation Modeling (TSSEM)
Estimate the heterogeneity (SD) of the parameter estimates of the
TSSEM object
Convert a Vector into a Symmetric Matrix
Conduct a Correlation/Covariance Structure Analysis with WLS
First Stage analysis of the univariate R (uniR) approach