Robust vce for heckman estimators

When using function heckit() from package ???sampleSelection???, is 
there anyway to make t-tests for the coefficients using robust 
covariance matrix estimator? By ???robust??? I mean something like if a 
had an object ???lm??? called ???reg??? and then used:

coeftest(reg, vcov = vcovHC(reg)).

You can do essentially the same for selection models with sandwich 
standard errors. For example:

library("AER")
library("sampleSelection")

data("PSID1976", package = "AER")
PSID1976$nwincome <- with(PSID1976, (fincome - hours * wage)/1000)

reg <- selection(participation ~ nwincome + education + experience +
     I(experience^2) + age + youngkids + oldkids,
   log(wage) ~ education + experience + I(experience^2),
   data = PSID1976)

coeftest(reg, vcov = sandwich)

Simple "sandwich" standard errors are available while other "vcovHC" 
standard errors (such as HC2, HC3, etc.) are not available for many models 
beyond linear regression.

Note also that I used the selection() function above which is typically 
preferable to heckit(), because the former produces the maximum likelihood 
foot. The latter by default produces the 2-step estimator which is 
nowadays usually only of interest in replication studies.

Best,
Z

I???m asking this because in Stata we could use function heckman and 
then use vce option ???robust???. We could do the same for cluster.

In a more general way, is there anyway to use another covariance matrix 
to make t-test (e.g. linear hypothesis) for heckit (selection) models?

Thanks,

Mateus Rabello
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