Norm Matloff, a statistics and computer science professor, writes about R, statistics, data science, and why neural networks resemble polynomial regression.
HN links it mainly for his polynomial regression argument about neural networks; one commenter has a low opinion of him, and the paper was revised after criticism.
in R loops are generally frown upon due to this issue.
BUT R is not slow when it comes to parallel processing or using Revolution Anayltics also has speed ups if you need it. R also has dplyr is speedy and the data.tables is even faster. I think the original Julia speed claims were a little biased to Julia and well there is plenty of awesome things about R, but "slow" isn't a far statement. There is a reason why R has grown so much.
Interesting argument for R usage:
https://matloff.wordpress.com/2014/05/21/r-beats-python-r-beats-julia-anyone-else-wanna-challenge-r/
> Also one could argue that the polynomial model is just a perceptron
One also can argue otherwise [1].
[1] https://matloff.wordpress.com/2018/06/20/neural-networks-are-essentially-polynomial-regression/
Incidentally, yesterday I submitted a wordpress blog post about some of these issues.
https://news.ycombinator.com/item?id=28817666
https://matloff.wordpress.com/2020/11/11/the-notion-of-double-descent/
This blog post by the same author looks pretty good too. "What Can Go Wrong: My Favorite Example" (27 April 2014)
http://matloff.wordpress.com/2014/04/27/what-can-go-wrong-my-favorite-example/
They revised the paper in response to earlier, uh, response.
https://matloff.wordpress.com/2018/07/01/update-on-polynomial-regression-in-lieu-of-neural-nets/
https://arxiv.org/abs/1806.06850
I've had a low opinion of Matloff since at least https://matloff.wordpress.com/2018/06/20/neural-networks-are-essentially-polynomial-regression/