Installing Catboost on MacOS Catalina

Roel M. Hogervorst


In this walkthrough I install catboost on the latest version of R on an older model macBook Pro with Macos Catalina 10.15.5 and I’m using homebrew.

The world of boosted tree models is growing over the past 4 years, the first revolution was with ‘XGBoost’ (eXtreme Gradient Boosting) in 2016, followed by ‘lightGBM’ (or LGBM) from January 2017 and later that year ‘catboost’. To use these C++ libraries in R you have to install them, and that process is slightly different for different architectures.


I’m also using the amazing RSwitch tool for the Mac to switch R versions: from 4.1.0 “experimental version”, to the latest stable version R 4.0.2 ‘Taking off again’ and an older version before that R 3.6.3 (2020-02-29) ‘Holding the Windsock’. I’m trying to install this library on all R versions, but if you use one version of R, this RSwitch tool is not necessary.

I’m mentioning all this because sometimes these things are really difficult to debug under different operating systems, and different R versions. For work I installed lightGBM on a macbook a year ago, and it was a lot more work then.


I’ve never installed catboost before so let’s see how it goes.

Catboost creates releases (bless them!) that you can install on you computer.

I followed the instruction for binary installation here on

You go to the releases page and find the release you want, the latest I think

For me the instructions were:

devtools::install_url('', INSTALL_opts = c("--no-multiarch"))

Please do verify that the links is correct for you, the version might have updated or you might point to different software. Another way to install this is to download the tgz file, inspect it with a virus scanner and use remotes::install_local()

(Re)start your R session and check if you can load the library library(catboost).

Installing on multiple R versions using RSwitch

Use RSwitch to switch R version to an older version of R and rerun the devtools argument above. (Re)start your R session and check if you can load the library library(catboost). repeat for all versions of R you want to check

And you are done.


I thought there would be issues with different versions of R, but the main issues are related to architecture: system libraries that you have to install on macos. After this installation the installation of the R wrapper is not really a big issue anymore, at least not for R 3.6, 4.0 and 4.1 (at the time of writing).


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