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  • 1
    FiniteDifferences.jl

    FiniteDifferences.jl

    High accuracy derivatives, estimated via numerical finite differences

    FiniteDifferences.jl estimates derivatives with finite differences. See also the Python package FDM. FiniteDiff.jl and FiniteDifferences.jl are similar libraries: both calculate approximate derivatives numerically. You should definitely use one or the other, rather than the legacy Calculus.jl finite differencing, or reimplementing it yourself. At some point in the future, they might merge, or one might depend on the other.
    Downloads: 1 This Week
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  • 2
    Infiltrator.jl

    Infiltrator.jl

    No-overhead breakpoints in Julia

    This package provides the @infiltrate macro, which acts as a breakpoint with negligible runtime performance overhead. Note that you cannot access other function scopes or step into further calls. Use an actual debugger if you need that level of flexibility. Running code that ends up triggering the @infiltrate REPL mode via inline evaluation in VS Code or Juno can cause issues, so it's recommended to always use the REPL directly. When the infiltration point is hit, it will drop you...
    Downloads: 0 This Week
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  • 3
    ReservoirComputing.jl

    ReservoirComputing.jl

    Reservoir computing utilities for scientific machine learning (SciML)

    ReservoirComputing.jl provides an efficient, modular and easy-to-use implementation of Reservoir Computing models such as Echo State Networks (ESNs). For information on using this package please refer to the stable documentation. Use the in-development documentation to take a look at not-yet-released features.
    Downloads: 0 This Week
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  • 4
    Wflow.jl

    Wflow.jl

    Hydrological modeling

    ... model concepts are available, which maximizes the use of open earth observation data, making it the hydrological model of choice for data-scarce environments. Based on gridded topography, soil, land use and climate data, wflow calculates all hydrological fluxes at any given grid cell in the model at a given time step.
    Downloads: 0 This Week
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  • 5
    Functors.jl

    Functors.jl

    Parameterise all the things

    Functors.jl provides tools to express a powerful design pattern for dealing with large/ nested structures, as in machine learning and optimization. For large machine learning models, it can be cumbersome or inefficient to work with parameters as one big, flat vector, and structs help manage complexity; but it is also desirable to easily operate over all parameters at once, e.g. for changing precision or applying an optimizer update step.
    Downloads: 0 This Week
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  • 6
    LineSearches.jl

    LineSearches.jl

    Line search methods for optimization and root-finding

    Line search methods for optimization and root-finding. This package provides an interface to line search algorithms implemented in Julia. The code was originally written as part of Optim, but has now been separated out to its own package.
    Downloads: 0 This Week
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  • 7
    Gaston.jl

    Gaston.jl

    A julia front-end for gnuplot

    Gaston is a Julia package for plotting. It provides an interface to gnuplot, a powerful plotting package available on all major platforms. The current stable release is v1.1.0, and it has been tested with Julia LTS (1.6) and stable (1.8), on Linux. Gaston should work on any platform that runs gnuplot.
    Downloads: 0 This Week
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  • 8
    Strategems

    Strategems

    Quantitative systematic trading strategy development and backtesting

    Strategems is a Julia package aimed at simplifying and streamlining the process of developing, testing, and optimizing algorithmic/systematic trading strategies. This package is inspired in large part by the quantstrat1,2 package in R, adopting a similar general structure to the building blocks that make up a strategy. Given the highly iterative nature of event-driven trading strategy development, Julia's high-performance design (particularly in the context of loops) and straightforward...
    Downloads: 0 This Week
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