Dependency Packages
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DuckDB.jl22645DuckDB is an analytical in-process SQL database management system
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DifferentialEquations.jl2841Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
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IJulia.jl2784Julia kernel for Jupyter
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Genie.jl2247🧞The highly productive Julia web framework
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JuMP.jl2210Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
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Turing.jl2026Bayesian inference with probabilistic programming.
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Gadfly.jl1900Crafty statistical graphics for Julia.
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Plots.jl1825Powerful convenience for Julia visualizations and data analysis
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Gen.jl1791A general-purpose probabilistic programming system with programmable inference
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MLJ.jl1779A Julia machine learning framework
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DataFrames.jl1725In-memory tabular data in Julia
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PyCall.jl1464Package to call Python functions from the Julia language
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Knet.jl1427Koç University deep learning framework.
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ModelingToolkit.jl1410An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
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Symbolics.jl1353Symbolic programming for the next generation of numerical software
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BrainFlow.jl1273BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
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AlphaZero.jl1232A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.
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CUDA.jl1193CUDA programming in Julia.
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Optim.jl1116Optimization functions for Julia
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NeuralNetDiffEq.jl966Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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NeuralPDE.jl966Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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Oceananigans.jl962🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
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Franklin.jl952(yet another) static site generator. Simple, customisable, fast, maths with KaTeX, code evaluation, optional pre-rendering, in Julia.
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JuDoc.jl952(yet another) static site generator. Simple, customisable, fast, maths with KaTeX, code evaluation, optional pre-rendering, in Julia.
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TensorFlow.jl884A Julia wrapper for TensorFlow
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DSGE.jl864Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
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DiffEqFlux.jl861Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
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DynamicalSystems.jl834Award winning software library for nonlinear dynamics and nonlinear timeseries analysis
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OnlineStats.jl831⚡ Single-pass algorithms for statistics
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Javis.jl827Julia Animations and Visualizations
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Weave.jl824Scientific reports/literate programming for Julia
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Documenter.jl807A documentation generator for Julia.
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JuliaDB.jl766Parallel analytical database in pure Julia
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PythonCall.jl763Python and Julia in harmony.
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Agents.jl728Agent-based modeling framework in Julia
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DiffEqTutorials.jl713Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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SciMLTutorials.jl713Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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Optimization.jl712Mathematical 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.
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GalacticOptim.jl712Mathematical 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.
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Gridap.jl691Grid-based approximation of partial differential equations in Julia
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