Kernel Based Gradient Matching for Parameter Inference in Ordinary Differential Equations
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 14
- Examples that run
- 11%1 run in a plain check · 8 never run
- Exported functions with examples
- 64%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing KGode adds 4 packages (2 with compiled code, 1.5 MB of source tarballs). Heaviest: mvtnorm 1.0 MB, pracma 389 KB, R6 63 KB.
Code & Tests
People & History
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- RR 4.6.0 released · 2026-04-24
- 1.0.5Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 1.0.42022-08-19 · diff ↗
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 1.0.3
- 1.0.22020-06-16 · diff ↗
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- 1.0.1
- 1.0.02017-10-23
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2017-10-23
- Total releases
- 6 / 9 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.2.0
- Download size
- 27 KB
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