smde: Sparse Multivariate Differential Equations

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Estimation of parameters in multivariate ordinary or stochastic differential equations using l1-regularized nonlinear least squares estimators.

Author
Niels Richard Hansen
Date of publication
2014-06-23 16:42:32
Maintainer
Niels Richard Hansen <Niels.R.Hansen@math.ku.dk>
License
GPL (>= 2)
Version
0.3.1

View on R-Forge

Man pages

coordinateDescent
l1-penalized least squares coordinate descent
coordinateDescentMF
l1-penalized matrix free least squares coordinate descent
coordinateDescentQuad
l1-penalized coordinate descent
dfEff
Effective degrees of freedom.
dfEffec
Computation of effective degrees of freedom.
dim
Dimensions of multivariate model
dpredict
Derivative of the predictor
fit
Fitting a multivariate process model
grad
Computation of the gradient of the loss function
loss
Computation of the squared error loss function
multModel
Multivariate process models constructor
penalty
Weighted l1-norm
predict.multModel
Prediction for multivariate models
progressBar
Yet another progress bar
response
Returns the response
risk
Computation of the squared error risk.
riskHat
Estimation of the risk.
smde
Sparse Multivariate Differential Equations
stepOptim
Stepwise optimization

Files in this package

smde/DESCRIPTION
smde/NAMESPACE
smde/R
smde/R/Optimization.R
smde/R/Simulation.R
smde/R/multModel.R
smde/R/progressBar.R
smde/R/smde-package.R
smde/man
smde/man/coordinateDescent.Rd
smde/man/coordinateDescentMF.Rd
smde/man/coordinateDescentQuad.Rd
smde/man/dfEff.Rd
smde/man/dfEffec.Rd
smde/man/dim.Rd
smde/man/dpredict.Rd
smde/man/fit.Rd
smde/man/grad.Rd
smde/man/loss.Rd
smde/man/multModel.Rd
smde/man/penalty.Rd
smde/man/predict.multModel.Rd
smde/man/progressBar.Rd
smde/man/response.Rd
smde/man/risk.Rd
smde/man/riskHat.Rd
smde/man/smde.Rd
smde/man/stepOptim.Rd
smde/src
smde/src/Makevars
smde/src/Makevars.win
smde/src/OUEulerScheme.cpp