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I wonder how close R was to also take over the scientific computing/machine learning space, instead of Python's numpy/scipy ecosystem.
It was never close. Its synthax is unintuitive and painful to learn as a science undergrad. If it hadn't been python it would have been another language.
R ecosystem provides amazing reproducible research ecosystem, even for statistical physics.
I wonder how close R was to also take over the scientific computing/machine learning space, instead of Python's numpy/scipy ecosystem.
Earlier quoted context omitted.
I wonder how close R was to also take over the scientific computing/machine learning space, instead of Python's numpy/scipy ecosystem.
I love and use R, but it never became the dominant ML in part because it has three (or more) different object systems and many libraries sort of use their own style. This makes it seem a bit disjointed, in a way that other languages don’t. The R community should have anointed one object system and made tidyverse a core part of R. All that said, R is fantastic and the depth of libraries is extensive. Libs are often wr…
Anyone can recommend a good and straightforward to understand -- general -- tutorial or book in Monte Carlo methods, for beginners?
When you try do that for real problems, it can sometimes be difficult to sample from complex probability distributions/models efficiently in a way that is representative. There are lots of tricks around that, like most topics it's a black-hole of details. But it still boils down to randomly testing options.
Look at the source code, even in C it's really short and simple: https://github.com/msuzen/isingLenzMC/blob/master/src/isingL...
Statisticians like to do this kind of intellectual inflation, there are many such scary terms with simple meanings: "Markov Chain" is a process who's next state depends only on the current state, "stochastic" is a straight-up synonym for "random"... Illegitimi non carborundum!
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One general purpose web framework away
I disagree. R is just not a very nice language. It has some really great statistical and data science packages that were well ahead of the competition 10-15 years ago. The web frameworks were good enough for dashboards and what most people were using R for. But if you wanted to write fast and elegant nom-vectorized code, R is really lacking. I left it for Julia for that reason.
Anyone can recommend a good and straightforward to understand -- general -- tutorial or book in Monte Carlo methods, for beginners?
It's not published yet, but already a classic. (Might be more intermediate than beginner, though.)
For something a bit more gentle, I also recommend chapter 29 of this book: https://www.inference.org.uk/mackay/itila/book.html
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I wonder how close R was to also take over the scientific computing/machine learning space, instead of Python's numpy/scipy ecosystem.
R is really not for production deployment. It lacks a lot of what made python popular, and its target users were radically different.
Anyone can recommend a good and straightforward to understand -- general -- tutorial or book in Monte Carlo methods, for beginners?
Once you understand and use this approach, you can figure out most other approaches you need to use.
Earlier quoted context omitted.
I wonder how close R was to also take over the scientific computing/machine learning space, instead of Python's numpy/scipy ecosystem.
It was never close. Its synthax is unintuitive and painful to learn as a science undergrad. If it hadn't been python it would have been another language.