Man pages for Newmi1988/seeds
Estimate Hidden Inputs using the Method of the Dynamic Elastic Net

BDENBayesian Dynamic Elastic Net
confidenceBandsGet the estimated confidence bands for the bayesian method
createCLangRootA function for creating a root function that can be used with...
createCompModelCreate compilable c-code of a model
createEventCreate an event function that sets states that are zero to a...
createFunctionscreates files for costate and augmented state
createRootA function for creating a root function that can be used with...
dynElasticNetestimating the optimal control using the dynamic elastic net
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
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 experiment.
outputEstimatesGet the estimated outputs
plotAnnoCreate annotated plot
plot-seedsPlot method for the S4 class resultsSeeds
print-seedsA default printing function for the resultsSeeds class
resultsSeeds-classResults Class for the Algorithms
seeds-packageseeds: Estimate Hidden Inputs using the Method of the Dynamic...
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
setYSet the vector with the initial (state) values
sgdnGreedy method for estimating a sparse solution
symbolicDiffSymbolic differentiation to create the adjoint equations
uvbDataUVB signal pathway
Newmi1988/seeds documentation built on Dec. 7, 2019, 8:54 a.m.