Package: MonteCarloSEM 2.0.0

MonteCarloSEM: Monte Carlo Simulation for Structural Equation Modeling

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) <doi:10.1007/BF02293811>. 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."

Authors:Fatih Orçan [aut, cre]

MonteCarloSEM_2.0.0.tar.gz
MonteCarloSEM_2.0.0.zip(r-4.7)MonteCarloSEM_2.0.0.zip(r-4.6)MonteCarloSEM_2.0.0.zip(r-4.5)
MonteCarloSEM_2.0.0.tgz(r-4.6-any)MonteCarloSEM_2.0.0.tgz(r-4.5-any)
MonteCarloSEM_2.0.0.tar.gz(r-4.7-any)MonteCarloSEM_2.0.0.tar.gz(r-4.6-any)
MonteCarloSEM_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
MonteCarloSEM/json (API)

# Install 'MonteCarloSEM' in R:

install.packages('MonteCarloSEM', repos = c('https://fatihorcan.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.30 score 2 stars 218 downloads 11 exports 8 dependencies

Last updated from:16152dbc2c. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	123
source / vignettes	OK	136
linux-release-x86_64	OK	114
macos-release-arm64	OK	132
macos-oldrel-arm64	OK	137
windows-devel	OK	77
windows-release	OK	77
windows-oldrel	OK	136
wasm-release	OK	90

Exports:categorize cov.mtx fcors.value fit.simulation loading.value MAR.data MCAR.data MNAR.data sim.categoric sim.normal sim.skewed

Dependencies:lattice lavaan MASS Matrix mnormt numDeriv pbivnorm quadprog

Help page	Topics
Generates Categorical Data Sets from Continuous Data.	categorize
Simulates Correlation matrix by a given SEM model.	cov.mtx
Specifies the Factor Correlation Matrix for a Model	fcors.value
Fits Structural Equation Models to Simulated Data Using lavaan.	fit.simulation
Specifies Factor Loading Values for a Model.	loading.value
Introduces Missing at Random (MAR) Values into Data Sets.	MAR.data
Introduces Missing Completely at Random (MCAR) Values into Data Sets.	MCAR.data
Introduces Missing Not at Random (MNAR) Values into Data Sets	MNAR.data
Simulates Categorical Data Sets Based on a Structural Equation Model (SEM).	sim.categoric
Simulates Data Sets Based on a Structural Equation Model (SEM).	sim.normal
Simulates Data Sets from a Structural Equation Model (SEM) with Normal or Non-Normal Distributions	sim.skewed

Package: MonteCarloSEM 2.0.0

MonteCarloSEM: Monte Carlo Simulation for Structural Equation Modeling

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)