pomp: Statistical Inference for Partially Observed Markov Processes

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Tools for working with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.

Author
Aaron A. King [aut, cre], Edward L. Ionides [aut], Carles Breto [aut], Stephen P. Ellner [ctb], Matthew J. Ferrari [ctb], Bruce E. Kendall [ctb], Michael Lavine [ctb], Dao Nguyen [ctb], Daniel C. Reuman [ctb], Helen Wearing [ctb], Simon N. Wood [ctb], Sebastian Funk [ctb], Steven G. Johnson [ctb]
Date of publication
2016-11-23 00:32:52
Maintainer
Aaron A. King <kingaa@umich.edu>
License
GPL (>= 2)
Version
1.10
URLs

View on CRAN

Man pages

abc
Estimation by approximate Bayesian computation (ABC)
bake
Tools for reproducible computations.
basic_probes
Some useful probes for partially-observed Markov processes
blowflies
Model for Nicholson's blowflies.
bsmc
The Liu and West Bayesian particle filter
bsplines
B-spline bases
dacca
Model of cholera transmission for historic Bengal.
design
Design matrices for pomp calculations
eulermultinom
The Euler-multinomial distributions and Gamma white-noise...
example
Examples of the construction of POMP models
gompertz
Gompertz model with log-normal observations.
kalman
Ensemble Kalman filters
logmeanexp
The log-mean-exp trick
lowlevel
pomp low-level interface
measles
Historical childhood disease incidence data
mif
Maximum likelihood by iterated filtering
mif2
IF2: Maximum likelihood by iterated, perturbed Bayes maps
nlf
Parameter estimation my maximum simulated quasi-likelihood...
ou2
Two-dimensional discrete-time Ornstein-Uhlenbeck process
package
Inference for partially observed Markov processes
parmat
Create a matrix of parameters
pfilter
Particle filter
pmcmc
The particle Markov chain Metropolis-Hastings algorithm
pomp
Constructor of the basic pomp object
pomp_fun
Definition and methods of the "pomp.fun" class
pomp_methods
Functions for manipulating, displaying, and extracting...
probe
Probe a partially-observed Markov process by computing...
proposals
MCMC proposal distributions
ricker
Ricker model with Poisson observations.
rw2
Two-dimensional random-walk process
sannbox
Simulated annealing with box constraints.
simulate_pomp
Simulations of a partially-observed Markov process
sir
Compartmental epidemiological models
spect
Power spectrum computation and spectrum-matching for...
traj_match
Parameter estimation by fitting the trajectory of a model's...

Files in this package

pomp
pomp/inst
pomp/inst/examples
pomp/inst/examples/dacca.R
pomp/inst/examples/ou2.R
pomp/inst/examples/rw2.R
pomp/inst/examples/ricker.R
pomp/inst/examples/bbs.R
pomp/inst/examples/gillespie.sir.R
pomp/inst/examples/gompertz.R
pomp/inst/examples/blowflies.R
pomp/inst/examples/euler.sir.R
pomp/inst/CITATION
pomp/inst/NEWS.Rd
pomp/inst/NEWS
pomp/inst/GPL
pomp/inst/doc
pomp/inst/doc/index.html
pomp/inst/include
pomp/inst/include/pomp.h
pomp/src
pomp/src/pfilter.c
pomp/src/dprior.c
pomp/src/Makevars
pomp/src/ou2.c
pomp/src/resample.c
pomp/src/pomp_mat.h
pomp/src/simulate.c
pomp/src/probe.c
pomp/src/euler.c
pomp/src/partrans.c
pomp/src/mif.c
pomp/src/probe_marginal.c
pomp/src/ssa.c
pomp/src/sobolseq.c
pomp/src/pomp.h
pomp/src/bspline.c
pomp/src/dprocess.c
pomp/src/blowfly.c
pomp/src/mif2.c
pomp/src/probe_acf.c
pomp/src/eulermultinom.c
pomp/src/ricker.c
pomp/src/userdata.c
pomp/src/sir.c
pomp/src/lookup_table.c
pomp/src/rprior.c
pomp/src/synth_lik.c
pomp/src/R_init_pomp.c
pomp/src/pomp_internal.h
pomp/src/rprocess.c
pomp/src/initstate.c
pomp/src/skeleton.c
pomp/src/trajectory.c
pomp/src/dmeasure.c
pomp/src/rmeasure.c
pomp/src/soboldata.h
pomp/src/gompertz.c
pomp/src/pomp_fun.c
pomp/src/SSA_simulator.c
pomp/src/probe_nlar.c
pomp/NAMESPACE
pomp/demo
pomp/demo/rw2.R
pomp/demo/sir.R
pomp/demo/gompertz.R
pomp/demo/00Index
pomp/demo/logistic.R
pomp/data
pomp/data/ewmeas.rda
pomp/data/LondonYorke.rda
pomp/data/ewcitmeas.rda
pomp/R
pomp/R/mif2_methods.R
pomp/R/kalman.R
pomp/R/rprocess_pomp.R
pomp/R/load.R
pomp/R/aaa.R
pomp/R/eulermultinom.R
pomp/R/safecall.R
pomp/R/profile_design.R
pomp/R/pomp.R
pomp/R/nlf.R
pomp/R/bsplines.R
pomp/R/trajectory_pomp.R
pomp/R/abc.R
pomp/R/example.R
pomp/R/sannbox.R
pomp/R/plot_pomp.R
pomp/R/sobol.R
pomp/R/nlf_guts.R
pomp/R/simulate_pomp.R
pomp/R/builder.R
pomp/R/pfilter.R
pomp/R/dprocess_pomp.R
pomp/R/generics.R
pomp/R/parmat.R
pomp/R/authors.R
pomp/R/pomp_class.R
pomp/R/probe_match.R
pomp/R/mif.R
pomp/R/slice_design.R
pomp/R/logmeanexp.R
pomp/R/pomp_methods.R
pomp/R/traj_match.R
pomp/R/pfilter_methods.R
pomp/R/mif2.R
pomp/R/csnippet.R
pomp/R/rmeasure_pomp.R
pomp/R/dmeasure_pomp.R
pomp/R/basic_probes.R
pomp/R/bsmc2.R
pomp/R/probe.R
pomp/R/initstate_pomp.R
pomp/R/plugins.R
pomp/R/abc_methods.R
pomp/R/nlf_funcs.R
pomp/R/bake.R
pomp/R/kalman_methods.R
pomp/R/covmat.R
pomp/R/bsmc.R
pomp/R/dprior_pomp.R
pomp/R/pmcmc_methods.R
pomp/R/pomp_fun.R
pomp/R/spect_match.R
pomp/R/rprior_pomp.R
pomp/R/skeleton_pomp.R
pomp/R/pmcmc.R
pomp/R/nlf_objfun.R
pomp/R/spect.R
pomp/R/minim.R
pomp/R/proposals.R
pomp/R/mif_methods.R
pomp/MD5
pomp/build
pomp/build/partial.rdb
pomp/DESCRIPTION
pomp/man
pomp/man/logmeanexp.Rd
pomp/man/blowflies.Rd
pomp/man/gompertz.Rd
pomp/man/spect.Rd
pomp/man/ricker.Rd
pomp/man/pomp_methods.Rd
pomp/man/measles.Rd
pomp/man/mif.Rd
pomp/man/proposals.Rd
pomp/man/simulate_pomp.Rd
pomp/man/pmcmc.Rd
pomp/man/abc.Rd
pomp/man/rw2.Rd
pomp/man/traj_match.Rd
pomp/man/ou2.Rd
pomp/man/mif2.Rd
pomp/man/bsplines.Rd
pomp/man/nlf.Rd
pomp/man/probe.Rd
pomp/man/lowlevel.Rd
pomp/man/sir.Rd
pomp/man/pfilter.Rd
pomp/man/bsmc.Rd
pomp/man/basic_probes.Rd
pomp/man/pomp.Rd
pomp/man/dacca.Rd
pomp/man/design.Rd
pomp/man/parmat.Rd
pomp/man/example.Rd
pomp/man/bake.Rd
pomp/man/package.Rd
pomp/man/pomp_fun.Rd
pomp/man/eulermultinom.Rd
pomp/man/sannbox.Rd
pomp/man/kalman.Rd