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MonteCarloSEM (version 2.0.0)

Monte Carlo Simulation for Structural Equation Modeling

Description

Provides tools to conduct Monte Carlo simulations under different conditions (e.g., varying sample size, data normality) for structural equation models (SEMs). Data can be simulated based on user-defined factor loadings and correlations, with optional non-normality added via Fleishman's power method (1978) . Once generated, models can be estimated using 'lavaan'. This package facilitates testing model performance across multiple simulation scenarios. When data generation is completed (or when generated data sets are given) model tests can also be run. Please cite as "Orçan, F. (2021). MonteCarloSEM An R Package to Simulate Data for SEM. International Journal of Assessment Tools in Education, 8 (3), 704-713."

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Version

Install

install.packages('MonteCarloSEM')

Monthly Downloads

227

Version

2.0.0

License

GPL-3

Maintainer

Fatih Orcan

Last Published

January 28th, 2026

Functions in MonteCarloSEM (2.0.0)

fcors.value

Specifies the Factor Correlation Matrix for a Model
cov.mtx

Simulates Correlation matrix by a given SEM model.
MCAR.data

Introduces Missing Completely at Random (MCAR) Values into Data Sets.
sim.normal

Simulates Data Sets Based on a Structural Equation Model (SEM).
MNAR.data

Introduces Missing Not at Random (MNAR) Values into Data Sets
MAR.data

Introduces Missing at Random (MAR) Values into Data Sets.
fit.simulation

Fits Structural Equation Models to Simulated Data Using lavaan.
sim.categoric

Simulates Categorical Data Sets Based on a Structural Equation Model (SEM).
categorize

Generates Categorical Data Sets from Continuous Data.
loading.value

Specifies Factor Loading Values for a Model.
sim.skewed

Simulates Data Sets from a Structural Equation Model (SEM) with Normal or Non-Normal Distributions