stan
nimpy
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stan | nimpy | |
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44 | 38 | |
2,521 | 1,416 | |
0.9% | - | |
9.4 | 5.8 | |
3 days ago | 3 months ago | |
C++ | Nim | |
BSD 3-clause "New" or "Revised" License | MIT License |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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stan
- Stan: Statistical modeling and high-performance statistical computation
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Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
Alternatives: stan and edward
- How often do you see Bayesian Statistics or Stan in the DS world? Essential skill or a nice to have?
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Rstan Package in ATPA
remove.packages(c("StanHeaders", "rstan")) install.packages("rstan", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
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[Q] Is there a method for adding random effects to an interval censored time to event model?
My approach to problems like this is to write down the proposed model mathematically first, in extreme detail. I find hierarchical form to be the easiest way to break it down piece by piece. Once I have the maths then I turn it into a Stan model. Last step is to use the Stan output to answer the research questions.
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HELP Conjugate Priors in Bayesian Regression in SPSS
Here is a good breakdown of recommendations from Andrew Gelman.
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Demand Planning
For instance my first choice in these cases is always a Bayesian inference tool like Stan. In my experience as someone who’s more of a programmer than mathematician/statistician, Bayesian tools like this make it much easier to not accidentally fool yourself with assumptions, and they can be pretty good at catching statistical mistakes.
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What do actual ML engineers think of ChatGPT?
I tend to be most impressed by tools and libraries. The stuff that has most impressed me in my time in ML is stuff like pytorch and Stan, tools that allow expression of a wide variety of statistical (and ML, DL models, if you believe there's a distinction) models and inference from those models. These are the things that have had the largest effect in my own work, not in the sense of just using these tools, but learning from their design and emulating what makes them successful.
- ChatGPT4 writes Stan code so I don’t have to
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How to get started learning modern AI?
oh its certainly used in practice. you should look into frameworks like Stan[1] and pyro[2]. i think bayesian models are seen as more explainable so they will be used in industries that value that sort of thing
[1] https://mc-stan.org/
nimpy
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Mojo is now available on Mac
I mean honestly, the closest language to Mojo really is Nim. In the latest Lex Fridman interview [0] when he talks about his ideas behind Mojo it pretty much sounds like he's describing Nim. Ok fair, he wants Mojo to be a full superset of Python, but honestly with nimpy [1] our Python interop is about as seamless as it can really be (without being a superset, which Mojo clearly is not yet). Even the syntax of Mojo looks a damn lot like Nim imo. Anyway, I guess he has the ability to raise enough funds to hire enough people to write his own language within ~2 years so as not have to follow random peoples whim about where to take the language. So I guess I can't blame him. But as someone who's pretty invested in the Nim community it's quite a shame to see such a hyped language receive so much attention by people who should really check out Nim. ¯\_(ツ)_/¯
[0]: https://youtu.be/pdJQ8iVTwj8?si=LfPSNDq8UKKIsJd3
[1]: https://github.com/yglukhov/nimpy
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Show HN: Pip Imports in Deno
You can also do this in Nim, which basically means you can write any program you could in Python with libraries in Nim. https://github.com/yglukhov/nimpy
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Nim v2.0 Released
Ones that have not been mentioned so far:
nlvm is an unofficial LLVM backend: https://github.com/arnetheduck/nlvm
npeg lets you write PEGs inline in almost normal PEG notation: https://github.com/zevv/npeg
futhark provides for much more automatic C interop: https://github.com/PMunch/futhark
nimpy allows calling Python code from Nim and vice versa: https://github.com/yglukhov/nimpy
questionable provides a lot of syntax sugar surrounding Option/Result types: https://github.com/codex-storage/questionable
ratel is a framework for embedded programming: https://github.com/PMunch/ratel
cps allows arbitrary procedure rewriting to continuation passing style: https://github.com/nim-works/cps
chronos is an alternative async/await backend: https://github.com/status-im/nim-chronos
zero-functional fixes some inefficiencies when chaining list operations: https://github.com/zero-functional/zero-functional
owlkettle is a declarative macro-oriented library for GTK: https://github.com/can-lehmann/owlkettle
A longer list can be found at https://github.com/ringabout/awesome-nim.
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Prospects of utilising Nim in scientific computation?
I use Python daily for its massive momentum for scientific stuff, but I also use Nim for everything else. Nim compiles to C, and making Python native modules with Nim is easy with Nimpy.
- Can't run compiled nim code in Python
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Returning to Nim from Python and Rust
If are a data scientist and come from python take a look at nimpy, a great way to just import python libraries and use them! https://github.com/yglukhov/nimpy Numpy, pandas, pytorch all usable in Nim.
Nim is the ultimate glue language, use libraries from anything: python, c, js, objc.
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Python's “Disappointing” Superpowers
I've come to really enjoy programming in Nim. Note that Nim is very different language despite sharing a similar syntax. However, I feel it keeps a lot of the "feel" of Python 2 days of being a fairly simple neat language but that lets you do things at compile time (like compile time duck typing).
There's a good Python -> Nim bridge: https://github.com/yglukhov/nimpy
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Dunder methods in nimpy
See this nimpy issue about it: https://github.com/yglukhov/nimpy/issues/43
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What language to move to from python to speed up algo?
It has pretty good integration with python, either for having your main code in python and writing small hot functions as nim and importing via nimporter or using python libraries in nim via nimpy.
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ABI compatibility in Python: How hard could it be?
Related: Nimpy[0] provides an easy way to write Python extensions in Nim, which manages the ABI side very well.
Python 2 is now gone, but until it was, Nimpy was an easy way to write Python extension modules that only needed to be compiled once, and would work with any of your installed Python 2 and Python 3. Magic.
[0] https://github.com/yglukhov/nimpy
What are some alternatives?
PyMC - Bayesian Modeling and Probabilistic Programming in Python
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Box - Python dictionaries with advanced dot notation access
rstan - RStan, the R interface to Stan
nimporter - Compile Nim Extensions for Python On Import!
Elo-MMR - Skill estimation systems for multiplayer competitions
scinim - The core types and functions of the SciNim ecosystem
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
nimpylib - Some python standard library functions ported to Nim
probability - Probabilistic reasoning and statistical analysis in TensorFlow
nimskull - An in development statically typed systems programming language; with sustainability at its core. We, the community of users, maintain it.