[pomp-announce] pomp version 0.41-1 released

[Aaron A. King]

Dear pomp users,

I've just submitted version pomp 0.41-1 to CRAN.  Source code and binaries 
should become available on the mirrors over the next few days.  There are lots 
of new features available in this version of pomp, including some that I think 
will really make pomp more intuitive to use.

Highlights include:

1. New arguments to 'mif', 'nlf', 'bsmc', 'pmcmc', 'probe-match', and 'traj-
match' allow transformation of parameters at estimation time.   When 
'transform=TRUE' in these commands ('transform.params=TRUE' for 'nlf'), the 
estimation is performed on the transformed parameter space.   This makes it 
unnecessary to "un-transform" model parameters within the user-specified 
'rprocess', 'dprocess', 'rmeasure', 'dmeasure', 'skeleton', and 'initializer' 
codes.   This is described and demonstrated in the 'intro_to_pomp' vignette 
(see the section on "Parameter Transformations".   Additionally, the data()-
loadable examples have been re-implemented to make use of this facility.

2. The Bayesian sequential Monte Carlo command 'bsmc' has been improved, due 
to the contributions of Pierre Jacob.   Specifically, 'bsmc' no longer reports 
a log-likelihood (which it never actually computed anyway) but a log-evidence.   
Codes for the latter computation were supplied by Pierre Jacob.   Also, 'bsmc' 
now returns not a list but an object of class 'bsmcd.pomp'.     An 
experimental 'plot' method for objects of this class now exists.   Also, the 
parameter posterior means are now stored in the 'params' slot of the 
'bsmcd.pomp' object: access them with the 'coef' command as usual.

3. New commands 'traj.match.objfun' and 'probe.match.objfun' have been added.   
These commands construct functions of one argument suitable for use as 
objective functions in 'optim'-like optimizers (iincluding 'subplex' and 
'sannbox').   Minimizing these functions solves the trajectory-matching 
problem and probe-matching problem, respectively.   This allows the user much 
greater flexibility in choice of optimization algorithm than is afforded by 
'traj.match' and 'probe.match'.

4. A new example, using data from an influenza outbreak in a British boarding 
school and an SIR model, has been included.     Do 'data(bbs)' to load it.

5. The 'sannbox' optimizer, which performs simulated annealing with box 
constraints, is now exported and available for general use.

See the package NEWS file for more details on these and other changes since the 
last CRAN release, v. 0.40-2.

All the best,

Aaron


-- 
Aaron A. King, Ph.D.
Ecology & Evolutionary Biology
Mathematics
Center for the Study of Complex Systems
University of Michigan
GPG Public Key: 0x6E74F51B