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Description

C++ Header Files for Stan.

The C++ header files of the Stan project are provided by this package, but it contains little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is primarily useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.

CVODES

Version 6.1.1 (Feb 2022)

Alan C. Hindmarsh, Radu Serban, Cody J. Balos, David J. Gardner, and Carol S. Woodward, Center for Applied Scientific Computing, LLNL

Daniel R. Reynolds, Department of Mathematics, Southern Methodist University

CVODES is a package for the solution of stiff and nonstiff ordinary differential equation (ODE) systems (initial value problem) given in explicit form

dy/dt = f(t,y,p), y(t0) = y0(p)

with sensitivity analysis capabilities (both forward and adjoint modes). CVODES provides a choice of two variable-order, variable-coefficient multistep methods, Adams-Moulton methods for non-stiff problems or BDF (Backward Differentiation Formula) methods in fixed-leading-coefficient form for stiff problems.

CVODES is part of the SUNDIALS Suite of Nonlinear and Differential/Algebraic equation Solvers which consists of ARKode, CVODE, CVODES, IDA, IDAS and KINSOL. It is written in ANSI standard C and can be used in a variety of computing environments including serial, shared memory, distributed memory, and accelerator-based (e.g., GPU) systems. This flexibility is obtained from a modular design that leverages the shared vector, matrix, linear solver, and nonlinear solver APIs used across SUNDIALS packages.

Documentation

See the CVODES User Guide and CVODES Examples document for more information about CVODES usage and the provided example programs respectively.

Installation

For installation instructions see the INSTALL_GUIDE or the "Installation Procedure" chapter in the CVODES User Guide.

Release History

Information on recent changes to CVODES can be found in the "Introduction" chapter of the CVODES User Guide and a complete release history is available in the "SUNDIALS Release History" appendix of the CVODES User Guide.

References

  • A. C. Hindmarsh, R. Serban, C. J. Balos, D. J. Gardner, D. R. Reynolds and C. S. Woodward, "User Documentation for CVODES v6.1.1," LLNL technical report UCRL-SM-208111, Feb 2022.

  • A. C. Hindmarsh and R. Serban, "Example Programs for CVODES v6.1.1," LLNL technical report UCRL-SM-208115, Feb 2022.

  • R. Serban and A. C. Hindmarsh, "CVODES: the Sensitivity-Enabled ODE solver in SUNDIALS," Proceedings of IDETC/CIE 2005, Sept. 2005, Long Beach, CA.

  • A. C. Hindmarsh, P. N. Brown, K. E. Grant, S. L. Lee, R. Serban, D. E. Shumaker, and C. S. Woodward, "SUNDIALS, Suite of Nonlinear and Differential/Algebraic Equation Solvers," ACM Trans. Math. Softw., 31(3), pp. 363-396, 2005.

Metadata

Version

2.32.10

License

Unknown

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