metaSEM v1.2.4


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Meta-Analysis using Structural Equation Modeling

A collection of functions for conducting meta-analysis using a structural equation modeling (SEM) approach via the 'OpenMx' and 'lavaan' packages. It also implements various procedures to perform meta-analytic structural equation modeling on the correlation and covariance matrices.


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The metaSEM package conducts univariate and multivariate meta-analyses using a structural equation modeling (SEM) approach via the OpenMx package. It also implements the two-stage SEM approach to conduct meta-analytic structural equation modeling on correlation/covariance matrices.

The stable version can be installed from CRAN by:


The developmental version can be installed from GitHub by:

## Install devtools package if it has not been installed yet
# install.packages("devtools")


Functions in metaSEM

Name Description
Boer16 Correlation Matrices from Boer et al. (2016)
Cheung00 Fifty Studies of Correlation Matrices used in Cheung and Chan (2000)
Becker92 Six Studies of Correlation Matrices reported by Becker (1992; 1995)
Becker09 Ten Studies of Correlation Matrices used by Becker (2009)
Bornmann07 A Dataset from Bornmann et al. (2007)
Aloe14 Multivariate effect sizes between classroom management self-efficacy (CMSE) and other variables reported by Aloe et al. (2014)
Berkey98 Five Published Trails from Berkey et al. (1998)
Becker94 Five Studies of Ten Correlation Matrices reported by Becker and Schram (1994)
BCG Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis
Becker83 Studies on Sex Differences in Conformity Reported by Becker (1983)
Cooper03 Selected effect sizes from Cooper et al. (2003)
Tenenbaum02 Correlation coefficients reported by Tenenbaum and Leaper (2002)
Hunter83 Fourteen Studies of Correlation Matrices reported by Hunter (1983)
VarCorr Extract Variance-Covariance Matrix of the Random Effects
Jaramillo05 Dataset from Jaramillo, Mulki & Marshall (2005)
Hox02 Simulated Effect Sizes Reported by Hox (2002)
HedgesOlkin85 Effects of Open Education Reported by Hedges and Olkin (1985)
Mak09 Eight studies from Mak et al. (2009)
Cooke16 Correlation Matrices from Cooke et al. (2016)
Cheung09 A Dataset from TSSEM User's Guide Version 1.11 by Cheung (2009)
Gleser94 Two Datasets from Gleser and Olkin (1994)
Kalaian96 Multivariate effect sizes reported by Kalaian and Raudenbush (1996)
checkRAM Check the correctness of the RAM formulation
is.pd Test Positive Definiteness of a List of Square Matrices
summary Summary Method for tssem1, wls, meta, and meta3X Objects
bootuniR2 Fit Models on the bootstrapped correlation matrices
issp05 A Dataset from ISSP (2005)
Nam03 Dataset on the Environmental Tobacco Smoke (ETS) on children's health
Cor2DataFrame Convert correlation or covariance matrices into a dataframe of correlations or covariances with their sampling covariance matrices
Gnambs18 Correlation Matrices from Gnambs, Scharl, and Schroeders (2018)
as.mxMatrix Convert a Matrix into MxMatrix-class
Diag Matrix Diagonals
Roorda11 Studies on Students' School Engagement and Achievement Reported by Roorda et al. (2011)
Nohe15 Correlation Matrices from Nohe et al. (2015)
Norton13 Studies on the Hospital Anxiety and Depression Scale Reported by Norton et al. (2013)
as.symMatrix Convert a Character Matrix with Starting Values to a Character Matrix without Starting Values
anova Compare Nested Models with Likelihood Ratio Statistic
list2matrix Convert a List of Symmetric Matrices into a Stacked Matrix
matrix2bdiag Convert a Matrix into a Block Diagonal Matrix
asyCov Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix
tssem1 First Stage of the Two-Stage Structural Equation Modeling (TSSEM)
coef Extract Parameter Estimates from various classes.
create.Fmatrix Create an F matrix to select observed variables Display the Pattern of Missing Data of a List of Square Matrices
wvs94a Forty-four Studies from Cheung (2013)
as.mxAlgebra Convert a Character Matrix into MxAlgebra-class
wls Conduct a Correlation/Covariance Structure Analysis with WLS
bdiagMat Create a Block Diagonal Matrix
create.mxMatrix Create a Vector into MxMatrix-class
create.vechsR Create a model implied correlation matrix with implicit diagonal constraints
impliedR Create or Generate the Model Implied Correlation or Covariance Matrices
plot Plot methods for various objects
indirectEffect Estimate the asymptotic covariance matrix of standardized or unstandardized indirect and direct effects
issp89 A Dataset from Cheung and Chan (2005; 2009)
smdMTS Compute Effect Sizes for Multiple Treatment Studies
meta3 Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation
Digman97 Factor Correlation Matrices of Big Five Model from Digman (1997)
smdMES Compute Effect Sizes for Multiple End-point Studies
readData Read External Correlation/Covariance Matrices
create.mxModel Create an mxModel
reml Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation
metaSEM-package Meta-Analysis using Structural Equation Modeling
homoStat Test the Homogeneity of Effect Sizes
wvs94b Forty-four Covariance Matrices on Life Satisfaction, Job Satisfaction, and Job Autonomy
lavaan2RAM Convert lavaan models to RAM models
Stadler15 Correlations from Stadler et al. (2015)
create.Tau2 Create a variance component of the heterogeneity of the random effects
Scalco17 Correlation Matrices from Scalco et al. (2017)
bdiagRep Create a Block Diagonal Matrix by Repeating the Input
pattern.n Display the Accumulative Sample Sizes for the Covariance Matrix
osmasemSRMR Calculate the SRMR in OSMASEM
meta Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation
bootuniR1 Parametric bootstrap on the univariate R (uniR) object
create.V Create a V-known matrix
reml3 Estimate Variance Components in Three-Level Univariate Meta-Analysis with Restricted (Residual) Maximum Likelihood Estimation
osmasem One-stage meta-analytic structural equation modeling
meta2semPlot Convert metaSEM objects into semPlotModel objects for plotting
print Print Methods for various Objects
rerun Rerun models via mxTryHard()
uniR2 Second Stage analysis of the univariate R (uniR) approach
rCor Generate Sample/Population Correlation/Covariance Matrices
osmasemR2 Calculate the R2 in OSMASEM
uniR1 First Stage analysis of the univariate R (uniR) approach
vec2symMat Convert a Vector into a Symmetric Matrix
vcov Extract Covariance Matrix Parameter Estimates from Objects of Various Classes
tssemParaVar Estimate the heterogeneity (SD) of the parameter estimates of the TSSEM object
vanderPol17 Dataset on the effectiveness of multidimensional family therapy in treating adolescents with multiple behavior problems
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Vignettes of metaSEM

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Last month downloads


Type Package
Date 2020-06-14
VignetteBuilder R.rsp
License GPL (>= 2)
LazyLoad yes
LazyData yes
ByteCompile yes
NeedsCompilation no
Packaged 2020-06-14 01:10:13 UTC; mikewlcheung
Repository CRAN
Date/Publication 2020-06-14 05:20:06 UTC

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