16 projects for "julia" with 2 filters applied:

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  • 1
    ConcurrentSim.jl

    ConcurrentSim.jl

    Discrete event process oriented simulation framework written in Julia

    A discrete event process-oriented simulation framework written in Julia inspired by the Python library SimPy. One of the longest-lived Julia packages (originally under the name SimJulia).
    Downloads: 1 This Week
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  • 2
    MLJ

    MLJ

    A Julia machine learning framework

    MLJ (Machine Learning in Julia) is a toolbox written in Julia providing a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing about 200 machine learning models written in Julia and other languages. The functionality of MLJ is distributed over several repositories illustrated in the dependency chart below. These repositories live at the JuliaAI umbrella organization.
    Downloads: 0 This Week
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  • 3
    AppleAccelerate.jl

    AppleAccelerate.jl

    Julia interface to the macOS Accelerate framework

    Julia interface to the macOS Accelerate framework. This provides a Julia interface to some of the macOS Accelerate frameworks. At the moment, this package provides access to Accelerate BLAS and LAPACK using the libblastrampoline framework, an interface to the array-oriented functions, which provide a vectorized form for many common mathematical functions.
    Downloads: 3 This Week
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  • 4
    GeoStats.jl

    GeoStats.jl

    An extensible framework for geospatial data science

    GeoStats.jl is a Julia framework for geospatial data science and geostatistical modeling. It’s fully implemented in Julia and designed to provide an extensible, high-performance stack that handles spatial domains, interpolation, simulation, learning, and visualization. The package is modular: it breaks out geometry, spatial domains, transforms, variograms, covariance models, and modeling into subpackages (e.g., GeoStatsBase, GeoStatsModels, GeoStatsTransforms).
    Downloads: 1 This Week
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  • 5
    InvertibleNetworks.jl

    InvertibleNetworks.jl

    A Julia framework for invertible neural networks

    Building blocks for invertible neural networks in the Julia programming language.
    Downloads: 0 This Week
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  • 6
    BenchmarkTools.jl

    BenchmarkTools.jl

    A benchmarking framework for the Julia language

    BenchmarkTools makes performance tracking of Julia code easy by supplying a framework for writing and running groups of benchmarks as well as comparing benchmark results. This package is used to write and run the benchmarks found in BaseBenchmarks.jl. The CI infrastructure for automated performance testing of the Julia language is not in this package but can be found in Nanosoldier.jl.
    Downloads: 0 This Week
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  • 7
    Trixi.jl

    Trixi.jl

    Trixi.jl: Adaptive high-order numerical simulations of hyperbolic PDEs

    Trixi.jl is a numerical simulation framework for hyperbolic conservation laws written in Julia. A key objective for the framework is to be useful to both scientists and students. Therefore, next to having an extensible design with a fast implementation, Trixi.jl is focused on being easy to use for new or inexperienced users, including the installation and postprocessing procedures.
    Downloads: 2 This Week
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  • 8
    ReTest.jl

    ReTest.jl

    Testing framework for Julia

    ReTest is a testing framework for Julia allowing defining tests in source files, whose execution is deferred and triggered on demand. This is useful when one likes to have definitions of methods and corresponding tests close to each other. This is also useful for code that is not (yet) organized as a package, and where one doesn't want to maintain a separate set of files for tests.
    Downloads: 0 This Week
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  • 9
    Clapeyron

    Clapeyron

    Framework for the development and use of fluid-thermodynamic models

    Welcome to Clapeyron! This module provides both a large library of thermodynamic models and a framework for one to easily implement their own models. Clapeyron provides a framework for the development and use of fluid-thermodynamic models, including SAFT, cubic, activity, multi-parameter, and COSMO-SAC.
    Downloads: 1 This Week
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  • 10
    Catlab.jl

    Catlab.jl

    A framework for applied category theory in the Julia language

    Catlab.jl is a framework for applied and computational category theory, written in the Julia language. Catlab provides a programming library and interactive interface for applications of category theory to scientific and engineering fields. It emphasizes monoidal categories due to their wide applicability but can support any categorical structure that is formalizable as a generalized algebraic theory. First and foremost, Catlab provides data structures, algorithms, and serialization for applied category theory. ...
    Downloads: 0 This Week
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  • 11
    Agents.jl

    Agents.jl

    Agent-based modeling framework in Julia

    Agents.jl is a pure Julia framework for agent-based modeling (ABM): a computational simulation methodology where autonomous agents react to their environment (including other agents) given a predefined set of rules. The simplicity of Agents.jl is due to the intuitive space-agnostic modeling approach we have implemented: agent actions are specified using generically named functions (such as "move agent" or "find nearby agents") that do not depend on the actual space the agents exist in, nor on the properties of the agents themselves. ...
    Downloads: 0 This Week
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  • 12
    MLJBase.jl

    MLJBase.jl

    Core functionality for the MLJ machine learning framework

    Repository for developers that provides core functionality for the MLJ machine learning framework. MLJ is a Julia framework for combining and tuning machine learning models. This repository provides core functionality for MLJ.
    Downloads: 0 This Week
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  • 13
    StructuralEquationModels.jl

    StructuralEquationModels.jl

    A fast and flexible Structural Equation Modelling Framework

    This is a package for Structural Equation Modeling in development. It is written for extensibility, that is, you can easily define your own objective functions and other parts of the model. At the same time, it is (very) fast. We provide fast objective functions, gradients, and for some cases hessians as well as approximations thereof. As a user, you can easily define custom loss functions. For those, you can decide to provide analytical gradients or use finite difference approximation /...
    Downloads: 1 This Week
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  • 14
    AbstractFFTs.jl

    AbstractFFTs.jl

    A Julia framework for implementing FFTs

    A general framework for fast Fourier transforms (FFTs) in Julia. This package is mainly not intended to be used directly. Instead, developers of packages that implement FFTs (such as FFTW.jl or FastTransforms.jl) extend the types/functions defined in AbstractFFTs. This allows multiple FFT packages to co-exist with the same underlying fft(x) and plan_fft(x) interface.
    Downloads: 0 This Week
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  • 15
    JuliaFEM.jl

    JuliaFEM.jl

    The JuliaFEM software library is a framework

    The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed...
    Downloads: 0 This Week
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  • 16
    Mocha.jl

    Mocha.jl

    Deep Learning framework for Julia

    Mocha.jl is a deep learning framework for Julia, inspired by the C++ Caffe framework. It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels directly in Julia and general auto-differentiation supports, the Mocha codebase becomes excessively old and primitive. ...
    Downloads: 0 This Week
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