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) <> 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>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README
CRAN checks: KGode results


Reference manual: KGode.pdf


Package source: KGode_1.0.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


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