Hello, running a mixed model in the package LME4, lmer()

Panel data, have about 322 time periods and 50 states, total data set is
approx 15K records and about 20  explanatory variables.  Not a very
large data set. 

We run random intercepts as well as random coefficients for about 10 of
the variables, the rest come in as fixed effects.   We are running into
a wall of time to execute these models.  As we add more random coefficients, it seems to slow down pretty dramatically. 

A sample specification of all random effects:

lmer(Y ~ 1 + (x_078 + x_079 + growth_st_index  +
            retail_st_index + Natl + econ_home_st_index +
            econ_bankruptcy +  index2_HO  + GPND_ST  | state),
            data = newData, doFit = TRUE)

Computation time is near 15 minutes.
System        ELAPSED        User
21.4            888.63        701.74


Does anyone have any ideas on way's to speed up lmer(), as well any
parallel implementations, or approaches/options to reduce computation time?

Running on a Linux 8 core machine, 16GB of ram, R 2.12















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