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Project.toml

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name = "StochasticDiffEq"
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uuid = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
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authors = ["Chris Rackauckas <accounts@chrisrackauckas.com>"]
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version = "6.52.0"
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version = "6.52.1"
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[deps]
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Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"

README.md

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and usable on its own, users interested in using this
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functionality should check out [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl).
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## API
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StochasticDiffEq.jl is part of the JuliaDiffEq common interface, but can be used independently of DifferentialEquations.jl. The only requirement is that the user passes an StochasticDiffEq.jl algorithm to `solve`. For example, we can solve the [SDE tutorial from the docs](https://diffeq.sciml.ai/stable/tutorials/sde_example/) using the `SRIW1()` algorithm:

test/noindex_tests.jl

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Base.similar(x::NoIndexArray, dims::Union{Integer, AbstractUnitRange}...) = NoIndexArray(similar(x.x, dims...))
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Base.copyto!(x::NoIndexArray, y::NoIndexArray) = NoIndexArray(copyto!(x.x, y.x))
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Base.copy(x::NoIndexArray) = NoIndexArray(copy(x.x))
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Base.zero(x::NoIndexArray) = NoIndexArray(zero(x.x))
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Base.fill!(x::NoIndexArray, y) = NoIndexArray(fill!(x.x, y))
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Base.mapreduce(f, op, x::NoIndexArray; kwargs...) = mapreduce(f, op, x.x; kwargs...)
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Base.any(f::Function, x::NoIndexArray; kwargs...) = any(f, x.x; kwargs...)

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