seeds: Estimate Hidden Inputs using the Dynamic Elastic Net

Algorithms to calculate the hidden inputs of systems of differential equations. These hidden inputs can be interpreted as a control that tries to minimize the discrepancies between a given model and taken measurements. The idea is also called the Dynamic Elastic Net, as proposed in the paper "Learning (from) the errors of a systems biology model" (Engelhardt, Froelich, Kschischo 2016) <doi:10.1038/srep20772>. To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: <>.

Version: 0.9.1
Depends: R (≥ 3.5.0)
Imports: deSolve (≥ 1.20), pracma (≥ 2.1.4), Deriv (≥ 3.8.4), Ryacas, stats, graphics, methods, mvtnorm, matrixStats, statmod, coda, MASS, ggplot2, tidyr, dplyr, Hmisc, R.utils, callr
Suggests: knitr, rmarkdown, rsbml
Published: 2020-07-14
DOI: 10.32614/CRAN.package.seeds
Author: Tobias Newmiwaka [aut, cre], Benjamin Engelhardt [aut]
Maintainer: Tobias Newmiwaka <tobias.newmiwaka at>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: seeds results


Reference manual: seeds.pdf
Vignettes: Seeds: Calculating the hidden inputs in a system


Package source: seeds_0.9.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): seeds_0.9.1.tgz, r-oldrel (arm64): seeds_0.9.1.tgz, r-release (x86_64): seeds_0.9.1.tgz, r-oldrel (x86_64): seeds_0.9.1.tgz
Old sources: seeds archive


Please use the canonical form to link to this page.