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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 |
Reference manual: | KGode.pdf |
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 imports: | shinyKGode |
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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.