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pomp: statistical inference for partially-observed Markov processes

pomp version 3.1 has just been released to CRAN and is on its way to a mirror near you. From this version pomp now requires at least version 4 of R.

Significant user-visible changes

• In mif2, the specification of particle numbers, Np, has changed. When Np is supplied as a function, Np(0) is the requested number of particles at the beginning of the time series, i.e., time t0. The previous behavior was that Np(1) specified the requested number of particles at time t0. This behavior now matches that of the other particle filtering algorithms, pfilter, wpfilter, bsmc2, and pmcmc.

• bsmc2 can now accept a variable number of particles, as do the other particle-filter based algorithms pfilter, wpfilter, pmcmc, and mif2.

• As promised from version 2.4.1, the tol and max.fail arguments have been removed completely from all particle-filtering algorithms, including pfilter, pmcmc, bsmc2, and mif2. See the the earlier blog post for more information.

• All instances of cond.loglik have been changed to cond.logLik, to remove a common source of typographical error. For the time being, the cond.loglik method will continue to be available, though deprecated.

• The long-deprecated functions onestep.dens, onestep.sim, discrete.time.sim, euler.sim, gillespie.sim, gillespie.hl.sim, conv.rec, and values have been removed. These have been replaced as follows:

Old function		Replacement
onestep.dens		direct specification of dprocess component
onestep.sim		onestep
discrete.time.sim		discrete_time
euler.sim		euler
gillespie.sim		gillespie
gillespie.hl.sim		gillespie_hl
conv.rec		traces
values		as.data.frame or as(x,"data.frame")

New features

• A new sequential importance sampling algorithm has been implemented as wpfilter. This is a generalization of the algorithm in pfilter in that it tracks the weights of the particles and allows the user to customize the resampling scheme. This should be considered to be in alpha stage: changes to the interface and the underlying algorithm may come without warning. Please give it a whirl and let me know what you think via the pomp issues page.

• A new saved.states method allows one to extract the saved states from a particle filter computation.

Under the hood

• The internal systematic_resample function now allows the user to specify the number of samples desired. Previously, these always had to be equal to the number of weights supplied.

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