CRAN/E | diffeqr

diffeqr

Solving Differential Equations (ODEs, SDEs, DDEs, DAEs)

Installation

About

An interface to 'DifferentialEquations.jl' from the R programming language. It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE), delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality, including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods. Supports GPUs, with support for CUDA (NVIDIA), AMD GPUs, Intel oneAPI GPUs, and Apple's Metal (M-series chip GPUs). 'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users. For more information, see Rackauckas and Nie (2017) doi:10.5334/jors.151.

Citation diffeqr citation info
github.com/SciML/diffeqr
System requirements Julia (>= 1.6), DifferentialEquations.jl, ModelingToolkit.jl

Key Metrics

Version 2.0.1
R ≥ 3.4.0
Published 2024-03-18 42 days ago
Needs compilation? no
License MIT
License File
CRAN checks diffeqr results

Downloads

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Maintainer

Maintainer

Christopher Rackauckas

me@chrisrackauckas.com

Authors

Christopher Rackauckas

aut / cre / cph

Material

README
NEWS
Reference manual
Package source

In Views

DifferentialEquations

Vignettes

Solving Differential-Algebraic Equations (DAE) in R with diffeqr
Solving Delay Differential Equations (DDE) in R with diffeqr
GPU-Accelerated Ordinary Differential Equations (ODE) in R with diffeqr
Solving Ordinary Differential Equations (ODE) in R with diffeqr
Solving Stochastic Differential Equations (SDE) in R with diffeqr

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

diffeqr archive

Depends

R ≥ 3.4.0

Imports

JuliaCall

Suggests

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knitr
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