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: 1.0.1
Depends: R (≥ 3.2.0)
Imports: R6, pracma, pspline, mvtnorm
Published: 2018-02-26
Author: Mu Niu [aut, cre]
Maintainer: Mu Niu <mu.niu at plymouth.ac.uk>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README
CRAN checks: KGode results

Downloads:

Reference manual: KGode.pdf
Package source: KGode_1.0.1.tar.gz
Windows binaries: r-devel: KGode_1.0.1.zip, r-devel-gcc8: KGode_1.0.1.zip, r-release: KGode_1.0.1.zip, r-oldrel: KGode_1.0.1.zip
OS X binaries: r-release: KGode_1.0.1.tgz, r-oldrel: KGode_1.0.1.tgz
Old sources: KGode archive

Reverse dependencies:

Reverse imports: shinyKGode

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