Estimate Hidden Inputs using the Dynamic Elastic Net
Bayesian Dynamic Elastic Net
Get the estimated confidence bands for the bayesian method
Create compilable c-code of a model
Greedy method for estimating a sparse solution
Get the estimated states
Gibbs Update
Get the estimated hidden inputs
Import SBML Models using the Bioconductor package 'rsbml'
Calculates the Log Likelihood for a new sample given the current state...
Componentwise Adapted Metropolis Hastings Sampler
Test dataset for demonstrating the bden algorithm.
Calculate the nominal solution of the model
A S4 class used to handle formatting ODE-Equation and calculate the ne...
A class to store the important information of an model.
estimating the optimal control using the dynamic elastic net
Get the estimated outputs
Create annotated plot
Plot method for the S4 class resultsSeeds
A default printing function for the resultsSeeds class
Results from the uvb dataset for examples
Results Class for the Algorithms
seeds: Estimate Hidden Inputs using the Dynamic Elastic Net
Set the vector with the initial (state) values
Set the inputs of the model.
set measurements of the model
Set the measurement equation for the model
Set the model equation
Set the model parameters
Set the standard deviation of the measurements
Automatic Calculation of optimal Initial Parameters
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: <https://bioconductor.org/packages/release/bioc/html/rsbml.html>.