avm
StatsBase.jl
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| avm | StatsBase.jl | |
|---|---|---|
| 2 | 5 | |
| 53 | 565 | |
| - | 1.2% | |
| 0.0 | 6.2 | |
| over 2 years ago | 14 days ago | |
| Common Lisp | Julia | |
| - | GNU General Public License v3.0 or later |
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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.
avm
- The Julia language has a number of correctness flaws
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cbaggers/rtg-math - a selection of the math routines most commonly needed for making realtime graphics in lisp (2, 3 and 4 component vectors, 3x3 and 4x4 matrices, quaternions, spherical and polar coordinates). [2019]
avm - Efficient and expressive arrayed vector math library with multi-threading and CUDA support. [MIT][200].
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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R user excited about Julia
The author identified some bugs and those were fixed. But they were all edge cases or footguns that are obviously bad to do, but allowed because Julia is a flexible language. For example, in this issue, the author overwrites the array they are sampling from. Which is obviously going to produce bad results.
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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.
- The Julia language has a number of correctness flaws
What are some alternatives?
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.
Lux.jl - Explicitly Parameterized Neural Networks in Julia
polisher - Infix notation to S-expression (Polish notation) translator for Common Lisp
Petalisp - Elegant High Performance Computing
numcl - Numpy clone in Common Lisp
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
cepl - Code Evaluate Play Loop
DSGE.jl - Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
physical-quantities - A common lisp library that provides a numeric type with optional unit and/or uncertainty for computations with automatic error propagation.
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/