JET.jl
RCall.jl
JET.jl | RCall.jl | |
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13 | 8 | |
694 | 313 | |
- | 1.0% | |
9.0 | 6.1 | |
3 days ago | 11 days ago | |
Julia | Julia | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
JET.jl
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Prospects of utilising Rust in scientific computation?
An informative discussion on julia forum. Have you tried using https://github.com/aviatesk/JET.jl to minimize type instabilities?
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Julia v1.9.0 has been released
For instance, https://github.com/aviatesk/JET.jl is still in its relative infancy, but it's played a big role in detecting quite a few potential bugs that had never been reported to use by users or caught in our testing infrastructure. There's also been a lot developments like interfaces to RR the time travelling debugger https://rr-project.org/ which helps us better understand and catch some very hard to debug non-deterministic bugs.
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Julia Computing Raises $24M Series A
Have you seen Shuhei Tadowaki's work on JET.jl (?)
If you're curious: https://github.com/aviatesk/JET.jl
This may seem more about performance (than IDE development) but Shuhei is one of the driving contributors behind developing the capabilities to use compiler capabilities for IDE integration -- and indeed JET.jl contains the kernel of a number of these capabilities.
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I Hate Programming Language Advocacy (2000)
This is sort of being done right now, as dynamic languages have begun to adopt gradual typing... at least Python and Julia, that I know of.
If something like [JET.jl](https://github.com/aviatesk/JET.jl) become ubiquitous in Julia, one could add a function that pointed out all the places in the code where types are not fully inferred by the compiler.
It'll never be quite the same level of safety as a static language, however.
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From Julia to Rust
- Pattern matching (sometimes you don't want the overhead of a method lookup)
[1]: https://github.com/aviatesk/JET.jl
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Julia is the best language to extend Python for scientific computing
You can use the `@code_warntype` macro to check for type stability, which is very helpful for detecting such performance pitfalls on single function level. In the future, https://github.com/aviatesk/JET.jl may give a more powerful way to do it.
- Jet.jl: experimental type checker for Julia
- Jet.jl: A WIP compile time type checker for Julia
RCall.jl
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Makie, a modern and fast plotting library for Julia
I don't use it personally, but RCall.jl[1] is the main R interop package in Julia. You could call libraries that have no equivalent in Julia using that and write your own analyses in Julia instead.
[1] https://github.com/JuliaInterop/RCall.jl
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Making Python 100x faster with less than 100 lines of Rust
You can have your cake and eat it with the likes of
* PythonCall.jl - https://github.com/cjdoris/PythonCall.jl
* NodeCall.jl - https://github.com/sunoru/NodeCall.j
* RCall.jl - https://github.com/JuliaInterop/RCall.jl
I tend to use Julia for most things and then just dip into another language’s ecosystem if I can’t find something to do the job and it’s too complex to build myself
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Interoperability in Julia
To inter-operate Julia with the R language, the RCall package is used. Run the following commands on the Julia REPL
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Convert Random Forest from Julia to R
https://github.com/JuliaInterop/RCall.jl may help
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I'm considering Rust, Go, or Julia for my next language and I'd like to hear your thoughts on these
If you need to bindings to your existing R packages then Julia is the way. Check out RCall.jl
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translate R code to Julia code
I have no experience with R, but maybe this will be of use: https://github.com/JuliaInterop/RCall.jl
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Julia 1.6: what has changed since Julia 1.0?
You can use RCall to use R from Julia: https://github.com/JuliaInterop/RCall.jl
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Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
I worked with R and Python during the last 3 years but learning and dabbling with Julia since 0.6. Since the availability of [PyCall.jl] and [RCall.jl], the transition to Julia can already be easier for Python/R users.
I agree that most of the time data wrangling is super confortable in R due to the syntax flexibility exploited by the big packages (tidyverse/data.table/etc). At the same time, Julia and R share a bigger heritage from Lisp influence that with Python, because R is also a Lisp-ish language (see [Advanced R, Metaprogramming]). My main grip from the R ecosystem is not that most of the perfomance sensitive packages are written in C/C++/Fortran but are written so deeply interconnect with the R environment that porting them to Julia that provide also an easy and good interface to C/C++/Fortran (and more see [Julia Interop] repo) seems impossible for some of them.
I also think that Julia reach to broader scientific programming public than R, where it overlaps with Python sometimes but provides the Matlab/Octave public with an better alternative. I don't expected to see all the habits from those communities merge into Julia ecosystem. On the other side, I think that Julia bigger reach will avoid to fall into the "base" vs "tidyverse" vs "something else in-between" that R is now.
[PyCall.jl]: https://github.com/JuliaPy/PyCall.jl
[RCall.jl]: https://github.com/JuliaInterop/RCall.jl
[Julia Interop]: https://github.com/JuliaInterop
[Advanced R, Metaprogramming] by Hadley Wickham: https://adv-r.hadley.nz/metaprogramming.html
What are some alternatives?
julia - The Julia Programming Language
Makie.jl - Interactive data visualizations and plotting in Julia
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
org-mode - This is a MIRROR only, do not send PR.
Metatheory.jl - Makes Julia reason with equations. General purpose metaprogramming, symbolic computation and algebraic equational reasoning library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
StaticArrays.jl - Statically sized arrays for Julia
Revise.jl - Automatically update function definitions in a running Julia session
HTTP.jl - HTTP for Julia
cmssw - CMS Offline Software
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia
PyCall.jl - Package to call Python functions from the Julia language