Description Usage Arguments Details Value See Also
Modelbased smoothing; estimation, objective criterion and derivatives.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  SplineEst.NewtRaph(coefs,times,data,lik,proc,pars,
control=list(reltol=1e12,maxit=1000,maxtry=10,trace=0))
SplineCoefsList(coefs,times,data,lik,proc,pars,sgn=1)
SplineCoefsErr(coefs,times,data,lik,proc,pars,sgn=1)
SplineCoefsDC(coefs,times,data,lik,proc,pars,sgn=1)
SplineCoefsDP(coefs,times,data,lik,proc,pars,sgn=1)
SplineCoefsDC2(coefs,times,data,lik,proc,pars,sgn=1)
SplineCoefsDCDP(coefs,times,data,lik,proc,pars,sgn=1)

coefs 
Vector giving the current estimate of the coefficients in the spline. 
times 
Vector observation times for the data. 
data 
Matrix of observed data values. 
lik 

proc 

pars 
Parameters to be used for the processes. 
sgn 
Is the minimizing (1) or maximizing (0)? 
control 
A list giving control parameters for NewtonRaphson optimization. It should contain

SplineEst.NewtRaph
performs a simple NewtonRaphson estimate for the optimal value of the coefficients.
This estimate lacks the convergence checks of other estimation packages, but may yeild a fast solution when needed.
SplineEst.NewtRaph 
Returns a list that is the result of the optimization with elements

SplineCoefsList 
Collates the gradient calculations and returns a list with elements

SplineCoefsErr 
The complete data log likelihood for the smooth; the inner optimization objective. 
SplineCoefsDC 
The derivative of 
SplineCoefsDP 
The derivative of 
SplineCoefsDC2 
The second derivative of 
SplineCoefsDCDP 
The second derivative of 
The output of gradients is in terms of an array with dimensions corresponding to derivatives. Derivatives with with respect to coefficients are given in dimensions before those that give derivatives with respect to parameters.
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