Usage
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)
Arguments
power
the value of $power$ such that the variance is
$var(Y) = phi * mu^power$
grid
the interpolation grid necessary for the given value of $power$
nx
the number of interpolation points in the $xi$ dimension
np
the number of interpolation points in the $power$ dimension
xix.lo
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 $1$.)
xix.hi
the higher value of the transformed $xi$ value used in the interpolation grid.
p.lo
the lower value of $p$ value used in the interpolation grid.
p.hi
the higher value of $p$ value used in the interpolation grid.
xix
the value of the transformed $xi$ at which a value is sought.
eps
the offset in computing the variance function in the saddlepoint approximation.
The default is eps=1/6
(as suggested by Nelder and Pregibon, 1987).