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
y
the vector of responses
power
the value of power such that the variance is
var(Y) = phi * mu^power
mu
the mean
phi
the dispersion
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).
providing 
.