Internal tweedie functions.
dtweedie.dlogfdphi(y, mu, phi, power)
dtweedie.logl(phi, y, mu, power)
dtweedie.logl.saddle( phi, power, y, mu, eps=0)
dtweedie.logv.bigp( y, phi, power)
dtweedie.logw.smallp(y, phi, power)
dtweedie.interp(grid, nx, np, xix.lo, xix.hi,p.lo, p.hi, power, xix)
dtweedie.jw.smallp(y, phi, power )
dtweedie.kv.bigp(y, phi, power)
dtweedie.series.bigp(power, y, mu, phi)
dtweedie.series.smallp(power, y, mu, phi)
stored.grids(power)
twpdf(p, phi, y, mu, exact, verbose, funvalue, exitstatus, relerr, its )
twcdf(p, phi, y, mu, exact, funvalue, exitstatus, relerr, its )
the vector of responses
the value of \(p\) such that the variance is \(\mbox{var}[Y]=\phi\mu^p\)
the mean
the dispersion
the interpolation grid necessary for the given value of \(p\)
the number of interpolation points in the \(\xi\) dimension
the number of interpolation points in the \(p\) dimension
the lower value of the transformed \(\xi\) value used in the interpolation grid. (Note that the value of \(\xi\) is from \(0\) to \(\infty\), and is transformed such that it is on the range \(0\) to \(1\).)
the higher value of the transformed \(\xi\) value used in the interpolation grid.
the lower value of \(p\) value used in the interpolation grid.
the higher value of \(p\) value used in the interpolation grid.
the value of the transformed \(\xi\) at which a value is sought.
the offset in computing the variance function in the saddlepoint approximation.
The default is eps=1/6
(as suggested by Nelder and Pregibon, 1987).
the Tweedie index parameter
a flag for the FORTRAN to use exact-zeros acceleration algorithmic the calculation (1 means to do so)
a flag for the FORTRAN: 1 means to be verbose
the value of the call returned by the FORTRAN code
the exit status returned by the FORTRAN code
an estimation of the relative error returned by the FORTRAN code
the number of iterations of the algorithm returned by the FORTRAN code
Peter Dunn (pdunn2@usc.edu.au)
These are not to be called by the user.
Nelder, J. A. and Pregibon, D. (1987). An extended quasi-likelihood function Biometrika, 74(2), 221--232. doi10.1093/biomet/74.2.221