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) <http://jmlr.org/proceedings/papers/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:	1.0.3
Depends:	R (≥ 3.2.0)
Imports:	R6, pracma, pspline, mvtnorm, graphics
Published:	2020-06-23
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_1.0.3.tar.gz
Windows binaries:	r-devel: KGode_1.0.3.zip, r-release: KGode_1.0.3.zip, r-oldrel: KGode_1.0.3.zip
macOS binaries:	r-release (arm64): KGode_1.0.3.tgz, r-oldrel (arm64): KGode_1.0.3.tgz, r-release (x86_64): KGode_1.0.3.tgz, r-oldrel (x86_64): KGode_1.0.3.tgz
Old sources:	KGode archive

Reverse dependencies:

Reverse imports:

shinyKGode

Linking:

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