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documentation improvements in version 3.1.1.7
05 August 2020
pomp development version 3.1.1.7 has just been released on the package github site. It contains a number of improvements to the package help pages. These are also available online (as the package manual).
version 3.1.1.1 released
19 July 2020
pomp development version 3.1.1.1 has just been released on the package github site.
This release introduces the profile_design function, which is intended to replace profileDesign.
The so-called profile design consists of a series of slices through a parameter space perpendicular to one or more coordinate axes.
The old behavior was to generate a single set of points in one of these slices (randomly according to a uniform distribution when type="runif" and according to a sub-random (Sobol’) sequence when type="sobol") and then copy these points across all slices.
Thus, in a projection perpendicular to the slices, all nprof generated points would lie atop one another.
The new behavior is to generate the points in each slice independently.
Thus, a projection perpendicular to the slices will display n*nprof points, where n is the number of slices.
For stylistic reasons, the other design functions, sliceDesign, runifDesign, and sobolDesign have been replaced by slice_design, runif_design, and sobol_design, respectively.
The older functions remain in place, but are deprecated and will be removed in a future release.
volunteer needed
09 July 2020
I’m looking for a volunteer who can maintain the pomp installation instructions for Mac users. Personally, I work pretty much exclusively on linux machines. I think I’m more or less able to keep up with the Windows installation instructions, but with respect to the Mac, I’m not keeping up. It seems I turned around and several things changed at once.
If there’s a Mac user out there, a veteran installer of pomp, who can edit/augment the Mac installation instructions on an ongoing basis, to keep up with advice from the R core team, probably including revising or replacing the gfortran installation instructions, it would a great service to the pomp community!
Please contact me if you’re interested.
version 3.1 released
05 July 2020
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. WhenNpis supplied as a function,Np(0)is the requested number of particles at the beginning of the time series, i.e., timet0. The previous behavior was thatNp(1)specified the requested number of particles at timet0. This behavior now matches that of the other particle filtering algorithms,pfilter,wpfilter,bsmc2, andpmcmc. -
bsmc2can now accept a variable number of particles, as do the other particle-filter based algorithmspfilter,wpfilter,pmcmc, andmif2. -
As promised from version 2.4.1, the
tolandmax.failarguments have been removed completely from all particle-filtering algorithms, includingpfilter,pmcmc,bsmc2, andmif2. See the the earlier blog post for more information. -
All instances of
cond.loglikhave been changed tocond.logLik, to remove a common source of typographical error. For the time being, thecond.loglikmethod 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, andvalueshave 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 inpfilterin 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.statesmethod allows one to extract the saved states from a particle filter computation.
Under the hood
- The internal
systematic_resamplefunction now allows the user to specify the number of samples desired. Previously, these always had to be equal to the number of weights supplied.
version 3.0.1.0 available on github
12 June 2020
pomp version 3.0.1.0 has been released: source code and binaries are available. This is a development release, anticipatory to the next CRAN version, which will be 3.1.
From this version, pomp requires at least version 4.0 of R.
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.
In particular, one can determine when resampling happens using the trigger option.
One sets trigger to a positive number:
when the effective sampling size is less than trigger * Np, resampling is triggered.
Thus setting trigger = 0 forbids resampling, while setting trigger to any number ≥0 forces resampling at every observation a la pfilter.
One can also determine the weight distribution of the resampled particles using the target option.
One sets target to a number in [0,1].
If target = α, say, and the weight of the i-th particle is wi, then it is resampled with weight wi1-α and, after resampling, carries weight wiα.
Thus, setting trigger = 1 and target = 0 reproduces the behavior of pfilter.
The new function wpfilter should be considered an alpha release:
both the interface and the underlying algorithms may change at any time without notice.
Please explore the function and give feedback via the pomp issues page.
For more information, see the help pages in the package manual.
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This software has been made possible by support from the U.S. National Science Foundation (Grants #EF-0545276, #EF-0430120), by the “Inference for Mechanistic Models” Working Group supported by the National Center for Ecological Analysis and Synthesis (a Center funded by N.S.F. (Grant #DEB-0553768), the University of California, Santa Barbara, and the State of California), and by the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security and the Fogarty International Center, U.S. National Institutes of Health. |