## Usage

gaitdnbinomial(a.mix = NULL, i.mix = NULL, d.mix = NULL,
a.mlm = NULL, i.mlm = NULL, d.mlm = NULL,
truncate = NULL, zero = c("size", "pobs", "pstr", "pdip"),
eq.ap = TRUE, eq.ip = TRUE, eq.dp = TRUE,
parallel.a = FALSE, parallel.i = FALSE, parallel.d = FALSE,
lmunb.p = "loglink",
lmunb.a = lmunb.p, lmunb.i = lmunb.p, lmunb.d = lmunb.p,
lsize.p = "loglink",
lsize.a = lsize.p, lsize.i = lsize.p, lsize.d = lsize.p,
type.fitted = c("mean", "munbs", "sizes", "pobs.mlm",
"pstr.mlm", "pdip.mlm", "pobs.mix", "pstr.mix", "pdip.mix",
"Pobs.mix", "Pstr.mix", "Pdip.mix", "nonspecial", "Numer",
"Denom.p", "sum.mlm.i", "sum.mix.i",
"sum.mlm.d", "sum.mix.d", "ptrunc.p", "cdf.max.s"),
gpstr.mix = ppoints(7) / 3,
gpstr.mlm = ppoints(7) / (3 + length(i.mlm)),
imethod = 1, mux.init = c(0.75, 0.5, 0.75, 0.5),
imunb.p = NULL, imunb.a = imunb.p,
imunb.i = imunb.p, imunb.d = imunb.p,
isize.p = NULL, isize.a = isize.p,
isize.i = isize.p, isize.d = isize.p,
ipobs.mix = NULL, ipstr.mix = NULL,
ipdip.mix = NULL, ipobs.mlm = NULL,
ipstr.mlm = NULL, ipdip.mlm = NULL,
byrow.aid = FALSE, ishrinkage = 0.95, probs.y = 0.35,
nsimEIM = 500, cutoff.prob = 0.999, eps.trig = 1e-7,
nbd.max.support = 4000, max.chunk.MB = 30)

## Arguments

lmunb.p, lmunb.a, lmunb.i, lmunb.d

Link functions pertaining to the mean parameters.
See `gaitdpoisson`

where `llambda.p`

etc. are
the equivalent.

lsize.p, lsize.a, lsize.i, lsize.d

Link functions pertaining to the `size`

parameters.
See `NegBinomial`

.

parallel.a, parallel.i, parallel.d

imethod, ipobs.mix, ipstr.mix, ipdip.mix

ipobs.mlm, ipstr.mlm, ipdip.mlm

mux.init

Numeric, of length 4.
General downward multiplier for initial values for
the sample proportions (MLEs actually).
See `gaitdpoisson`

.
The fourth value corresponds to `size`

.

imunb.p, imunb.a, imunb.i, imunb.d

isize.p, isize.a, isize.i, isize.d

nsimEIM, cutoff.prob, eps.trig

nbd.max.support, max.chunk.MB