Implementation of 'CausalKinetiX', a framework for learning stable structures in kinetic systems. Apart from the main functions CausalKinetiX() and CausalKinetiX.modelranking() it includes functions to generate data from three simulations models, which can be used to benchmark structure learning methods for linear ordinary differential equation models. A detailed description of the underlying methods as well as details on the examples are given in Pfister, Bauer and Peters (2018) <arXiv:1810.11776>.
| Version: | 0.2.1 |
| Imports: | fda, cvTools, quadprog, randomForest, deSolve, stats, graphics, pspline, utils, glmnet, sundialr (≥ 0.1.3) |
| Published: | 2019-06-20 |
| Author: | Niklas Pfister [aut, cre], Stefan Bauer [aut], Jonas Peters [aut] |
| Maintainer: | Niklas Pfister <niklas.pfister at stat.math.ethz.ch> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | CausalKinetiX results |
| Reference manual: | CausalKinetiX.pdf |
| Package source: | CausalKinetiX_0.2.1.tar.gz |
| Windows binaries: | r-devel: CausalKinetiX_0.2.1.zip, r-release: CausalKinetiX_0.2.1.zip, r-oldrel: CausalKinetiX_0.2.1.zip |
| macOS binaries: | r-release: CausalKinetiX_0.2.1.tgz, r-oldrel: CausalKinetiX_0.2.1.tgz |
| Old sources: | CausalKinetiX archive |
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