31–40 of 70 posts
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.
The issue with R is that there is too much dsl. This is great for one-off analysis but makes building a cohesive large code base really difficult.
Why is this worth posting? Simulating a 1D Ising model is a homework exercise for undergrads.
Anyone can recommend a good and straightforward to understand -- general -- tutorial or book in Monte Carlo methods, for beginners?
https://archive.org/details/TheMonte-carloMethodlittleMathem...
Does the use of "Statistical Physics" as opposed to "Statistical Mechanics" indicates a European author or a broader scope?
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…
> and made tidyverse a core part of R.
Not a tidyverse fan. It doesn't scale well.
Learn data.table, which has a much more R-like interface and is fast fast fast even for large data sizes. More powerful and more expressive than pandas, and again, faster
See https://cran.r-project.org/web/packages/data.table/vignettes...
Does anybody have a reason this particular repo might be of special interest today? It appears to be the methods from something published a decade ago, and has had a handful of commits since then, including yesterday. There's not a ton of stars on the github, but certainly more stars than most scientific methods get.
Does anybody have a reason this particular repo might be of special interest today? It appears to be the methods from something published a decade ago, and has had a handful of commits since then, including yesterday. There's not a ton of stars on the github, but certainly more stars than most scientific methods get.
Apart from intellectual appeal,
(1) there was a new paper from Google about quantum ergodicity, see https://doi.org/10.48550/arXiv.2506.10191 . So in general tech community can benefit a lot from understanding ergodicity via this package and see hands on how it is implemented, see Vignette as well, https://cran.r-project.org/web/packages/isingLenzMC/vignette...
(2) The repo is part of ergodicity research that is now revisited from classical point of view. actually new commits are significant, a new dataset is generated. See, https://zenodo.org/records/17151290 , so reproducibility is amazing even after so many years.