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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.

Versions across snapshots

VersionRepositoryFileSize
1.0.5 rolling linux/jammy R-4.5 KGode_1.0.5.tar.gz 393.4 KiB
1.0.5 rolling linux/noble R-4.5 KGode_1.0.5.tar.gz 393.1 KiB
1.0.5 rolling source/ R- KGode_1.0.5.tar.gz 26.6 KiB
1.0.5 latest linux/jammy R-4.5 KGode_1.0.5.tar.gz 393.4 KiB
1.0.5 latest linux/noble R-4.5 KGode_1.0.5.tar.gz 393.1 KiB
1.0.5 latest source/ R- KGode_1.0.5.tar.gz 26.6 KiB
1.0.5 2026-04-26 source/ R- KGode_1.0.5.tar.gz 26.6 KiB
1.0.5 2026-04-23 source/ R- KGode_1.0.5.tar.gz 26.6 KiB
1.0.5 2026-04-09 windows/windows R-4.5 KGode_1.0.5.zip 395.4 KiB
1.0.4 2025-04-20 source/ R- KGode_1.0.4.tar.gz 25.7 KiB

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