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) . To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: <>.


Reference manual

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0.9.1 by Tobias Newmiwaka, a year ago

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Authors: Tobias Newmiwaka [aut, cre] , Benjamin Engelhardt [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports deSolve, pracma, Deriv, Ryacas, stats, graphics, methods, mvtnorm, matrixStats, statmod, coda, MASS, ggplot2, tidyr, dplyr, Hmisc, R.utils, callr

Suggests knitr, rmarkdown, rsbml

See at CRAN