Building glmms to handle zero-inflated continuous data in R - what options are available? (especially relating to hurdle/mixture models)

Hi,

I have a zero-inflated continuous data set and want to build a glmm in R to
analyze it (I have both fixed and random effects). However, because my data
are continuous, I am discovering that this is not a simple task.
Zero-inflation options in glmmABMD are not appropriate because my data are
continuous and I don't know what other packages exist that allow for
zero-inflated glmms with continuous data.

I tried implementing the Tweedie distribution using packages tweedie and
cplm, but these are a poor fit to my data.

I think hurdle or mixture models might be especially useful for my data.
When I modeled the non-zero continuous data separately from the
zero/non-zero data, I get a very good fit to the data. However, I am stuck
at how to integrate the two models. There seem to be packages in R that do
this for count data but I have not found them for continuous data.

I have been reading previous r-sig-ecology posts about this and find a lot
of information from 2008-2012. I was wondering in the last few years if
there have been developments in and if there are now available: (1)
packages or techniques for easily implementing glmms for zero-inflated data
in R, and (2) are there any good packages for mixture or hurdle models in R
that allow for continuous data (i.e., how can I integrate the two models
for the zero/non-zero versus non-zero continuous data)?

Thank you very much for any help!
Karan

Karan J. Odom
Ph.D. Candidate, Biological Sciences
University of Maryland, Baltimore County
1000 Hilltop Circle
Baltimore, MD 21250

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