Showing 4 open source projects for "model-builder"

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    ModelingToolkitStandardLibrary.jl

    ModelingToolkitStandardLibrary.jl

    A standard library of components to model the world and beyond

    The ModelingToolkit Standard Library is a standard library of components to model the world and beyond.
    Downloads: 5 This Week
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  • 2
    GLM.jl

    GLM.jl

    Generalized linear models in Julia

    GLM.jl is a Julia package for fitting linear and generalized linear models (GLMs) with a syntax and functionality familiar to users of R or other statistical environments. It is part of the JuliaStats ecosystem and is tightly integrated with StatsModels.jl for formula handling, and Distributions.jl for specifying error families. The package supports modeling through both formula-based (e.g. @formula) and matrix-based interfaces, allowing both high-level convenience and low-level control....
    Downloads: 7 This Week
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  • 3
    PromptingTools.jl

    PromptingTools.jl

    Streamline your life using PromptingTools.jl

    PromptingTools.jl is a Julia-based toolkit designed to simplify prompt engineering and unify interactions with multiple large language model providers through a consistent interface. It focuses on reducing the complexity of prompt creation by introducing templating systems, macros, and reusable functions that standardize how prompts are constructed and executed. The library provides a family of ai* functions that handle tasks such as generation, embeddings, classification, and data extraction, all following a consistent structure. ...
    Downloads: 3 This Week
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  • 4
    Gen.jl

    Gen.jl

    A general-purpose probabilistic programming system

    ...Gen features an easy-to-use modeling language for writing down generative models, inference models, variational families, and proposal distributions using ordinary code. But it also lets users migrate parts of their model or inference algorithm to specialized modeling languages for which it can generate especially fast code. Users can also hand-code parts of their models that demand better performance. Neural network inference is fast, but can be inaccurate on out-of-distribution data, and requires expensive training.
    Downloads: 1 This Week
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