Parallel computing is easy to use in R thanks to packages like doParallel.

“parallel” Package The parallel package in R can perform tasks in parallel by providing the ability to allocate cores to R. The working involves finding the number of cores in the system and allocating all of them or a subset to make a cluster.

In D. Kaplan (Ed. ): Understand what parallel computing is and when it may be useful; Understand how parallelism can work; Review sequential loops and *apply functions; Understand and use the parallel package multicore functions; Support for Parallel computation in R Support for parallel computation, including by forking (taken from package multicore), by sockets (taken from package snow) and random-number generation. Journal of Applied Psychology, 68(3), 363-373. However, before we decide to parallelize our code, still we should remember that there is a trade-off between simplicity and performance.
Quick Intro to Parallel Computing in R Matt Jones 7/25/2017. Although many great sources for parallel computing in R exist, few explain the concepts in such a basic way that anyone can get started. Due to the high-level nature of R and the strong open source developer community, it is remarkably simple to parallelise both basic and more complex tasks. So if your script runs a few seconds, probably it's not worth to bother yourself. Today is a good day to start parallelizing your code.

Determining the number of factors in exploratory and confirmatory factor analysis. Hoyle, R. H. and Duvall, J. L. (2004).

There are various packages in R which allow parallelization. Learning Outcomes.
I've been using the parallel package since its integration with R (v. 2.14.0) and its much easier than it at first seems. Drasgow, F. and Lissak, R. (1983) Modified parallel analysis: a procedure for examining the latent dimensionality of dichotomously scored item responses. Functions in …

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