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KGode: Kernel Based Gradient Matching for Parameter Inference in Ordinary Differential Equations

The kernel ridge regression and the gradient matching algorithm proposed in Niu et al. (2016) <https://proceedings.mlr.press/v48/niu16.html> and the warping algorithm proposed in Niu et al. (2017) <doi:10.1007/s00180-017-0753-z> are implemented for parameter inference in differential equations. Four schemes are provided for improving parameter estimation in odes by using the odes regularisation and warping.

Version: 1.0.4
Depends: R (≥ 3.2.0)
Imports: R6, pracma, pspline, mvtnorm, graphics
Published: 2022-08-19
DOI: 10.32614/CRAN.package.KGode
Author: Mu Niu [aut, cre]
Maintainer: Mu Niu <mu.niu at glasgow.ac.uk>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README
CRAN checks: KGode results

Documentation:

Reference manual: KGode.pdf

Downloads:

Package source: KGode_1.0.4.tar.gz
Windows binaries: r-devel: KGode_1.0.4.zip, r-release: KGode_1.0.4.zip, r-oldrel: KGode_1.0.4.zip
macOS binaries: r-release (arm64): KGode_1.0.4.tgz, r-oldrel (arm64): KGode_1.0.4.tgz, r-release (x86_64): KGode_1.0.4.tgz, r-oldrel (x86_64): KGode_1.0.4.tgz
Old sources: KGode archive

Reverse dependencies:

Reverse imports: shinyKGode

Linking:

Please use the canonical form https://CRAN.R-project.org/package=KGode to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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