Dennis Boos, Kevin Matthew, Jason Osborne
Computes Monte Carlo standard errors for summaries of Monte Carlo output. Summaries and their standard errors are based on columns of Monte Carlo simulation output. Dennis D. Boos and Jason A. Osborne (2015) <doi:10.1111/insr.12087>.

Automatic Parallelized Monte Carlo Simulations
Simplifies Monte Carlo simulation studies by automatically
setting up loops to run over parameter grids and parallelising
the Monte Carlo repetitions. It also generates LaTeX tables.

knitr, rmarkdown, testthat, dplyr, ggplot2

person(given = "Fatih", family = "Orcan", role = c("aut", "cre"), email = "fatihorcan84@gmail.com", comment = c(ORCID = "0000-0003-1727-0456"))
Monte Carlo simulation allows to test different conditions given to the correct structural equation models. This package runs Monte Carlo simulations under different conditions (such as sample size or normality of data). Within the package data sets can be simulated and run based on the given model.
First, continuous and normal data sets are generated based on the given model. Later Fleishman's power method (1978) <DOI:10.1007/BF02293811> is used to add non-normality if exists.
When data generation is completed (or when generated data sets are given) model test can also be run.

Monte Carlo simulation allows to test different conditions given to the correct structural equation models. This package runs Monte Carlo simulations under different conditions (such as sample size or normality of data). Within the package data sets can be simulated and run based on the given model.
First, continuous and normal data sets are generated based on the given model. Later Fleishman's power method (1978) <DOI:10.1007/BF02293811> is used to add non-normality if exists.
When data generation is completed (or when generated data sets are given) model test 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."