Package: magi 1.2.4

Shihao Yang

magi: MAnifold-Constrained Gaussian Process Inference

Provides fast and accurate inference for the parameter estimation problem in Ordinary Differential Equations, including the case when there are unobserved system components. Implements the MAGI method (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) <doi:10.1073/pnas.2020397118>. A user guide is provided by the accompanying software paper Wong, Yang, and Kou (2024) <doi:10.18637/jss.v109.i04>.

Authors:Shihao Yang [aut, cre], Samuel W.K. Wong [aut], S.C. Kou [ctb, cph]

magi_1.2.4.tar.gz
magi_1.2.4.tar.gz(r-4.5-noble)magi_1.2.4.tar.gz(r-4.4-noble)
magi_1.2.4.tgz(r-4.4-emscripten)magi_1.2.4.tgz(r-4.3-emscripten)
magi.pdf |magi.html✨
magi/json (API)

# Install 'magi' in R:
install.packages('magi', repos = 'https://cloud.r-project.org')
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • FNdat - Dataset of noisy observations from the FitzHugh-Nagumo (FN) equations

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

3.00 score 251 downloads 23 exports 12 dependencies

Last updated 9 months agofrom:137fd1f7ba. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 22 2025
R-4.5-linux-x86_64OKMar 22 2025
R-4.4-linux-x86_64OKMar 22 2025

Exports:calCovfnmodelDthetafnmodelDxfnmodelODEgpcovgpmeangpsmoothinggpsmoothllikhes1logmodelDthetahes1logmodelDxhes1logmodelODEhes1modelDthetahes1modelDxhes1modelODEis.magioutputmagioutputMagiPosteriorMagiSolverptransmodelDthetaptransmodelDxptransmodelODEsetDiscretizationtestDynamicalModel

Dependencies:BHclideSolvegluegridBasegridExtragtablelifecycleRcppRcppArmadillorlangroptim

magi-vignette

Rendered frommagi-vignette.Rmdusingknitr::rmarkdownon Mar 22 2025.

Last update: 2024-05-06
Started: 2021-07-27

Citation

To cite magi in publications use:

Wong SWK, Yang S, Kou SC (2024). “magi: A Package for Inference of Dynamic Systems from Noisy and Sparse Data via Manifold-Constrained Gaussian Processes.” Journal of Statistical Software, 109(4), 1–47. doi:10.18637/jss.v109.i04.

Corresponding BibTeX entry: 

  @Article{,
    title = {{magi}: A Package for Inference of Dynamic Systems from
      Noisy and Sparse Data via Manifold-Constrained {G}aussian
      Processes},
    author = {Samuel W. K. Wong and Shihao Yang and S. C. Kou},
    journal = {Journal of Statistical Software},
    year = {2024},
    volume = {109},
    number = {4},
    pages = {1--47},
    doi = {10.18637/jss.v109.i04},
  }