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pomp: statistical inference for partially-observed Markov processes

Version 4.1 of pomp is now on CRAN and coming soon to a mirror near you. This release contains quite a number of changes, including new features, some user-interface improvements, and some changes to the examples provided with the package. As the increment to the major version number suggests, a few of these changes are not backwardly compatible with versions 3.X.

New features

Monte Carlo-adjusted profile likelihood

• Computation of Monte Carlo-adjusted profile likelihood is now facilitated by the new mcap function.

Deterministic trajectories and trajectory matching

• The behavior of trajectory now fully conforms to the behavior of other pomp elementary algorithms. In particular, one can now add, remove, or modify basic model components in a call to trajectory just as one can with simulate, pfilter, probe, etc. Before version 4, additional arguments to trajectory (i.e., those passed via ...) were passed on to the deSolve ODE integrator in the case of continuous-time deterministic skeletons (i.e., vectorfields) and ignored in the case of discrete-time skeletons (i.e., maps). As of version 4, in order to adjust ODE integrator settings it is necessary to use the ode_control argument of trajectory. This behavior matches that of traj_objfun.

• It is now possible to create a ‘pomp’ object from scratch using trajectory, by specifying the rinit and skeleton components. Prior to version 4, in order to do so, it was necessary to first create a dummy data set, make a call to pomp, and pass the resulting object to trajectory. This can now be achieved in one call, just as in the other elementary algorithms.

• In a call to trajectory, the user now has the option to have the results returned as one or ‘pomp’ objects. This is controlled by the format argument as in simulate. The default return-value format for trajectory now matches that of simulate.

Ensemble Kalman filter methods

• Two new basic model components have been introduced. These are:
  • emeasure, which computes the expectation of the observable variables conditional on the latent state;
  • vmeasure, which computes the covariance matrix of the observables given the latent state.

• The ensemble adjusted Kalman filter (eakf) has been refactored. It now makes use of the new emeasure and vmeasure basic components to compute an approximation of the linear relationship between the latent state and the observed variables.

• The basic (linear, Gaussian) Kalman filter is now available as kalmanFilter.

User interface changes

• It is now possible to plot lists of ‘pomp’ and ‘pomp’-derived objects using a single call to plot.

• The rbetabinom and dbetabinom functions, long present as part of the C API, are now available as R functions.

• parmat can now take a data frame of parameters and convert it into a matrix suitable for furnishing to the params argument of any pomp function. Also, parmat now takes an optional argument, names, which allows the user to name the parameter sets.

• It is now possible to adjust the parameters in an objfun (objective function) object using coef(object)<-value as with ‘pomp’ objects.

• The forecast method now works for ‘pfilterd_pomp’ objects (i.e., results of pfilter, pmcmc, or mif2 computations).

• Independent realizations of the stochastic processes modeled in pomp are now distinguished by different values of the variable .id. This behavior is now uniform throughout the package.

• bake and stew now send messages instead of warnings when they recompute an archive due to a change in code or dependencies.

• The names of the variables returned by as.data.frame, as applied to a pfilterd.pomp object, have changed.

Changes to examples

• The SIR examples sir() and sir2() now use a negative binomial measurement model, in keeping with practices we have been recommending in our short course.

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