Solving Differential Equations in R: Package deSolve
In this paper we present the R package <b>deSolve</b> to solve initial value problems (IVP) written as ordinary differential equations (ODE), differential algebraic equations (DAE) of index 0 or 1 and partial differential equations (PDE), the latter solved using the method of lines appro...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2010-02-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | http://www.jstatsoft.org/v33/i09/paper |
| _version_ | 1818128661399732224 |
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| author | Karline Soetaert Thomas Petzoldt R. Woodrow Setzer |
| author_facet | Karline Soetaert Thomas Petzoldt R. Woodrow Setzer |
| author_sort | Karline Soetaert |
| collection | DOAJ |
| description | In this paper we present the R package <b>deSolve</b> to solve initial value problems (IVP) written as ordinary differential equations (ODE), differential algebraic equations (DAE) of index 0 or 1 and partial differential equations (PDE), the latter solved using the method of lines approach. The differential equations can be represented in R code or as compiled code. In the latter case, R is used as a tool to trigger the integration and post-process the results, which facilitates model development and application, whilst the compiled code significantly increases simulation speed. The methods implemented are efficient, robust, and well documented public-domain Fortran routines. They include four integrators from the <b>ODEPACK</b> package (LSODE, LSODES, LSODA, LSODAR), DVODE and DASPK2.0. In addition, a suite of Runge-Kutta integrators and special-purpose solvers to efficiently integrate 1-, 2- and 3-dimensional partial differential equations are available. The routines solve both stiff and non-stiff systems, and include many options, e.g., to deal in an efficient way with the sparsity of the Jacobian matrix, or finding the root of equations. In this article, our objectives are threefold: (1) to demonstrate the potential of using R for dynamic modeling, (2) to highlight typical uses of the different methods implemented and (3) to compare the performance of models specified in R code and in compiled code for a number of test cases. These comparisons demonstrate that, if the use of loops is avoided, R code can efficiently integrate problems comprising several thousands of state variables. Nevertheless, the same problem may be solved from 2 to more than 50 times faster by using compiled code compared to an implementation using only R code. Still, amongst the benefits of R are a more flexible and interactive implementation, better readability of the code, and access to R’s high-level procedures. <b>deSolve</b> is the successor of package <b>odesolve</b> which will be deprecated in the future; it is free software and distributed under the GNU General Public License, as part of the R software project. |
| first_indexed | 2024-12-11T07:36:48Z |
| format | Article |
| id | doaj.art-19b72c411eae4c8ea93696d25ae3a3f2 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-11T07:36:48Z |
| publishDate | 2010-02-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-19b72c411eae4c8ea93696d25ae3a3f22022-12-22T01:15:41ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602010-02-013309Solving Differential Equations in R: Package deSolveKarline SoetaertThomas PetzoldtR. Woodrow SetzerIn this paper we present the R package <b>deSolve</b> to solve initial value problems (IVP) written as ordinary differential equations (ODE), differential algebraic equations (DAE) of index 0 or 1 and partial differential equations (PDE), the latter solved using the method of lines approach. The differential equations can be represented in R code or as compiled code. In the latter case, R is used as a tool to trigger the integration and post-process the results, which facilitates model development and application, whilst the compiled code significantly increases simulation speed. The methods implemented are efficient, robust, and well documented public-domain Fortran routines. They include four integrators from the <b>ODEPACK</b> package (LSODE, LSODES, LSODA, LSODAR), DVODE and DASPK2.0. In addition, a suite of Runge-Kutta integrators and special-purpose solvers to efficiently integrate 1-, 2- and 3-dimensional partial differential equations are available. The routines solve both stiff and non-stiff systems, and include many options, e.g., to deal in an efficient way with the sparsity of the Jacobian matrix, or finding the root of equations. In this article, our objectives are threefold: (1) to demonstrate the potential of using R for dynamic modeling, (2) to highlight typical uses of the different methods implemented and (3) to compare the performance of models specified in R code and in compiled code for a number of test cases. These comparisons demonstrate that, if the use of loops is avoided, R code can efficiently integrate problems comprising several thousands of state variables. Nevertheless, the same problem may be solved from 2 to more than 50 times faster by using compiled code compared to an implementation using only R code. Still, amongst the benefits of R are a more flexible and interactive implementation, better readability of the code, and access to R’s high-level procedures. <b>deSolve</b> is the successor of package <b>odesolve</b> which will be deprecated in the future; it is free software and distributed under the GNU General Public License, as part of the R software project.http://www.jstatsoft.org/v33/i09/paperordinary differential equationspartial differential equationsdifferential algebraic equationsinitial value problemsRFortranC |
| spellingShingle | Karline Soetaert Thomas Petzoldt R. Woodrow Setzer Solving Differential Equations in R: Package deSolve Journal of Statistical Software ordinary differential equations partial differential equations differential algebraic equations initial value problems R Fortran C |
| title | Solving Differential Equations in R: Package deSolve |
| title_full | Solving Differential Equations in R: Package deSolve |
| title_fullStr | Solving Differential Equations in R: Package deSolve |
| title_full_unstemmed | Solving Differential Equations in R: Package deSolve |
| title_short | Solving Differential Equations in R: Package deSolve |
| title_sort | solving differential equations in r package desolve |
| topic | ordinary differential equations partial differential equations differential algebraic equations initial value problems R Fortran C |
| url | http://www.jstatsoft.org/v33/i09/paper |
| work_keys_str_mv | AT karlinesoetaert solvingdifferentialequationsinrpackagedesolve AT thomaspetzoldt solvingdifferentialequationsinrpackagedesolve AT rwoodrowsetzer solvingdifferentialequationsinrpackagedesolve |