Man pages for seeds
Estimate Hidden Inputs using the Dynamic Elastic Net

BDENBayesian Dynamic Elastic Net
confidenceBandsGet the estimated confidence bands for the bayesian method
createCompModelCreate compilable c-code of a model
DENGreedy method for estimating a sparse solution
estiStatesGet the estimated states
GIBBS_updateGibbs Update
hiddenInputsGet the estimated hidden inputs
importSBMLImport SBML Models using the Bioconductor package 'rsbml'
LOGLIKELIHOOD_funcCalculates the Log Likelihood for a new sample given the...
MCMC_componentComponentwise Adapted Metropolis Hastings Sampler
ModelTest dataset for demonstrating the bden algorithm.
nominalSolCalculate the nominal solution of the model
odeEquations-classA S4 class used to handle formatting ODE-Equation and...
odeModel-classA class to store the important information of an model.
optimal_control_gradient_descentestimating the optimal control using the dynamic elastic net
outputEstimatesGet the estimated outputs
plotAnnoCreate annotated plot
plotseedsPlot method for the S4 class resultsSeeds
print-seedsA default printing function for the resultsSeeds class
resResults from the uvb dataset for examples
resultsSeeds-classResults Class for the Algorithms
seeds-packageseeds: Estimate Hidden Inputs using the Dynamic Elastic Net
setInitStateSet the vector with the initial (state) values
setInputSet the inputs of the model.
setMeasset measurements of the model
setMeasFuncSet the measurement equation for the model
setModelEquationSet the model equation
setParmsSet the model parameters
setSdSet the standard deviation of the measurements
SETTINGSAutomatic Calculation of optimal Initial Parameters
uvbDataUVB signal pathway
uvbModelAn object of the odeModel Class
seeds documentation built on July 14, 2020, 1:07 a.m.