v2.0.1

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 2.0.0
-Date: 2023-10-20 04:58:15 UTC
-SHA: d10dd51ac4f1d09be4302618ea1884286a0a6094
+Version: 2.0.1
+Date: 2024-03-18 11:35:30 UTC
+SHA: cfcc2d4bf8781368fa99b0ed8587493836e4136f

DESCRIPTION

@@ -1,24 +1,24 @@
-Package: diffeqr
-Type: Package
-Title: Solving Differential Equations (ODEs, SDEs, DDEs, DAEs)
-Version: 2.0.0
-Authors@R: person("Christopher", "Rackauckas", email = "[email protected]", role = c("aut", "cre", "cph"))
-Description: 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>.
-Depends: R (>= 3.4.0)
-Encoding: UTF-8
-License: MIT + file LICENSE
-URL: https://github.com/SciML/diffeqr
-SystemRequirements: Julia (>= 1.6), DifferentialEquations.jl, ModelingToolkit.jl
-Imports:
-  JuliaCall
-RoxygenNote: 7.1.1
-Suggests: testthat,
-    knitr,
-    rmarkdown
-VignetteBuilder: knitr
+Package: diffeqr
+Type: Package
+Title: Solving Differential Equations (ODEs, SDEs, DDEs, DAEs)
+Version: 2.0.1
+Authors@R: person("Christopher", "Rackauckas", email = "[email protected]", role = c("aut", "cre", "cph"))
+Description: 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>.
+Depends: R (>= 3.4.0)
+Encoding: UTF-8
+License: MIT + file LICENSE
+URL: https://github.com/SciML/diffeqr
+SystemRequirements: Julia (>= 1.6), DifferentialEquations.jl, ModelingToolkit.jl
+Imports:
+  JuliaCall
+RoxygenNote: 7.1.1
+Suggests: testthat,
+    knitr,
+    rmarkdown
+VignetteBuilder: knitr

NEWS.md

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+## Release v2.0.1
+
+Updated to support ModelingToolkit v9 from the Julia side with the JIT compilation.
+
 ## Release v2.0.0
 
 Support new DiffEqGPU syntax. This requires passing a backend. Supports NVIDIA CUDA, Intel OneAPI,