I think as a general rule, you're going to have the best experience with the least fussing by using

MRO and adding a line to your .Rprofile that sets your CRAN repository to RStudio's.

```
options(repos = c(CRAN = "https://cran.rstudio.com/"))
```

This makes sure you get the latest package updates versus the MRO default behavior, which installs the version of packages that were available on a specific date. This is good for some people, but not most.

If you want to test the speed difference, open up R and run

```
source("http://r.research.att.com/benchmarks/R-benchmark-25.R")
```

in each version you want to compare and check out the results.

With all of this said, there's another way to get a roughly equivalent speed boost on Mac. See this link for some command line instructions for linking macOS's built-in fast BLAS library to your R installation: https://statistics.berkeley.edu/computing/blas

I always make sure I have a fast BLAS with my install (MRO on Windows/Linux, the aforementioned method on macOS) because there are times where you'll notice the difference if you're run the same code without the faster BLAS. I noticed it the most on a series of regressions that required a big-ish set of matrix algebra calculations — something like 5000 observations and 15 predictors. There are some things where it either makes no difference or such a small one that you won't notice it, but it's nice to have.