StatsBase.jl
OffsetArrays.jl
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StatsBase.jl | OffsetArrays.jl | |
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5 | 7 | |
559 | 190 | |
0.0% | 3.2% | |
6.2 | 6.7 | |
18 days ago | about 2 months ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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StatsBase.jl
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Downloading packages to Julia 0.7
so finally I tried running Pkg.add(Pkg.PackageSpec(url="https://github.com/JuliaStats/StatsBase.jl", rev="v0.24.0")) but encountered an error saying in needed to download dependencies like DataStructures.
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Julia ranks in the top most loved programming languages for 2022
Well, out of the issues mentioned, the ones still open can be categorized as (1) aliasing problems with mutable vectors https://github.com/JuliaLang/julia/issues/39385 https://github.com/JuliaLang/julia/issues/39460 (2) not handling OffsetArrays correctly https://github.com/JuliaStats/StatsBase.jl/issues/646, https://github.com/JuliaStats/StatsBase.jl/issues/638, https://github.com/JuliaStats/Distributions.jl/issues/1265 https://github.com/JuliaStats/StatsBase.jl/issues/643 (3) bad interaction of buffering and I/O redirection https://github.com/JuliaLang/julia/issues/36069 (4) a type dispatch bug https://github.com/JuliaLang/julia/issues/41096
So if you avoid mutable vectors and OffsetArrays you should generally be fine.
As far as the argument "Julia is really buggy so it's unusable", I think this can be made for any language - e.g. rand is not random enough, Java's binary search algorithm had an overflow, etc. The fixed issues have tests added so they won't happen again. Maybe copying the test suites from libraries in other languages would have caught these issues earlier, but a new system will have more bugs than a mature system so some amount of bugginess is unavoidable.
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The Julia language has a number of correctness flaws
Most of these seem to be about packages in the ecosystem (which, after clicking through all links, actually almost all got fixed in a very timely manner, sometimes already in a newer version of the packages than the author was using), not about the language itself. Other than that, the message of this seems to be "newer software has bugs", which yes is a thing..?
For example, the majority of issues referenced are specific to a single package, StatsBase.jl - which apparently was written before OffsetArrays.jl was a thing and thus is known to be incompatible:
> Yes, lots of JuliaStats packages have been written before offset axes existed. Feel free to make a PR adding checks.
https://github.com/JuliaStats/StatsBase.jl/issues/646#issuec...
OffsetArrays.jl
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The Julia language has a number of correctness flaws
Similar correctness issues are a big part of the reason that, several years ago, I submitted a series of pull requests to Julia so that its entire test suite would run without memory errors under Valgrind, save for a few that either (i) we understood and wrote suppressions for, or (ii) we did not understand and had open issues for. Unfortunately, no one ever integrated Valgrind into the CI system, so the test suite no longer fully runs under it, last time I checked. (The test suite took nearly a day to run under Valgrind on a fast desktop machine when it worked, so is infeasible for every pull request, but could be done periodically, e.g. once every few days.)
Even a revived effort on getting core Julia tests to pass under Valgrind would not do much to help catch correctness bugs due to composing different packages in the ecosystem. For that, running in testing with `--check-bounds=yes` is probably a better solution, and much quicker to execute as well. (see e.g. https://github.com/JuliaArrays/OffsetArrays.jl/issues/282)
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-🎄- 2021 Day 6 Solutions -🎄-
You might be interested in OffsetArrays.jl.
- PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
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Why does Julia adopt 1-based index?
Counting starts at one, as do most vector/matrix/tensor indices. If it bothers you too much, see OffsetArrays.jl and Arrays with custom indices.
- some may hate it, some may love it
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Evcxr: A Rust REPL and Jupyter Kernel
No need for another version, Julia supports custom indices by default. Check out https://docs.julialang.org/en/v1/devdocs/offset-arrays/ and https://github.com/JuliaArrays/OffsetArrays.jl
What are some alternatives?
StarWarsArrays.jl - Arrays indexed as the order of Star Wars movies
Lux.jl - Explicitly Parameterized Neural Networks in Julia
Petalisp - Elegant High Performance Computing
Optimization.jl - Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
TwoBasedIndexing.jl - Two-based indexing
julia - The Julia Programming Language
DSGE.jl - Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
TailRec.jl - A tail recursion optimization macro for julia.
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
evcxr