Merge pull request #1 from bbolker/master

various code/packaging cleanup
All tpm CI's for HMMs

The EM likelihood is the same except with no parameter transformations.
I added a likelihood function that takes all parameters on the natural
scale and use it to get the hessian.  I then use a constraint matrix to
get the correct SEs for the tpm parameters.

Next up, delta method for the stationary distributions and turning HSMM
CI's back on for the tpm.  No idea what to do about the tps that are not
directly maximized when nstates>2
Previously forgot to transpose tpm inside mllk function

also added check for negative standard errors, setting them to NA.  This
happens in the shifted lognormal example for the encamped shift
parameter, which is on the 0 boundary
slight modification

Sometimes getting negative variances.  I need to look into this.  I
wouldn't trust CIs until this is looked at more carefully.
trap problems with CI calculation

return Hessian as part of results