Package: diffeqr 2.0.1

Christopher Rackauckas

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

An interface to 'DifferentialEquations.jl' <https://diffeq.sciml.ai/dev/> 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>.

Authors:Christopher Rackauckas [aut, cre, cph]

diffeqr_2.0.1.tar.gz
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diffeqr_2.0.1.tgz(r-4.4-any)diffeqr_2.0.1.tgz(r-4.3-any)
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diffeqr.pdf |diffeqr.html✨
diffeqr/json (API)
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Peer review:

Bug tracker:https://github.com/sciml/diffeqr/issues

On CRAN:

daeddedelay-differential-equationsdifferential-algebraic-equationsdifferential-equationsodeordinary-differential-equationsscientific-machine-learningscimlsdestochastic-differential-equations

4 exports 140 stars 4.78 score 8 dependencies 31 scripts 471 downloads

Last updated 6 months agofrom:7a14a42abf. Checks:OK: 7. Indexed: yes.

Exports:diffeq_setupdiffeqgpu_setupjitoptimize_odejitoptimize_sde

Dependencies:evaluatehighrJuliaCallknitrRcpprjsonxfunyaml