RDocumentation
Moon
Learn R
Search all packages and functions
⚠️
There's a newer version (5.8) of this package.
Take me there.
pomp (version 0.34-1)
Statistical inference for partially observed Markov processes
Description
Inference methods for partially-observed Markov processes
Copy Link
Copy
Link to current version
Version
Version
5.8
5.7
5.6
5.5
5.4
5.3
5.2
5.1
4.7
4.6
4.5
4.4
4.3
4.2
4.1
3.6
3.5
3.4
3.3
3.2
3.1
2.8
2.7
2.6
2.5
2.4
2.3
2.2
2.1
1.19
1.18.8.2
1.18.8.1
1.18.7.1
1.18.4.1
1.18.1.1
1.18
1.17.3.1
1.17.1.1
1.17
1.16.3.2
1.16.2.4
1.16.2.1
1.16.1.2
1.16
1.15.4.1
1.15.3.2
1.15.2.2
1.15
1.14.2.1
1.14.1.5
1.14.1.1
1.14
1.13.4.2
1.13.3.1
1.13.2.1
1.13
1.12
1.10
1.9
1.8
1.7
1.6
1.4.1.1
1.3.1.1
1.2.1.1
1.1.1.1
0.65-1
0.53-5
0.53-1
0.49-2
0.49-1
0.45-8
0.43-8
0.43-4
0.43-1
0.42-4
0.42-1
0.41-3
0.41-1
0.40-2
0.40-1
0.39-4
0.39-3
0.39-2
0.39-1
0.38-5
0.38-3
0.38-2
0.38-1
0.37-1
0.36-7
0.36-5
0.36-4
0.36-2
0.36-1
0.35-1
0.34-2
0.34-1
0.33-1
0.32-6
0.32-5
0.32-1
0.31-1
0.30-1
0.29-5
0.29-2
0.28-5
0.28-2
0.27-2
0.27-1
0.26-3
0.25-7
0.25-4
0.24-7
0.24-5
0.24-1
0.23-6
0.23-5
0.23-2
0.23-1
0.22-6
0.22-5
0.22-4
0.21-3
0.20-8
0.20-4
0.20-2
0.19-1
0.18-3
0.18-2
0.18-1
0.17-3
0.17-2
Down Chevron
Install
install.packages('pomp')
Monthly Downloads
2,519
Version
0.34-1
License
GPL (>= 2)
Maintainer
Aaron King
Last Published
October 10th, 2010
Functions in pomp (0.34-1)
Search functions
LondonYorke
Historical childhood disease incidence data
pomp-methods
Methods of the "pomp" class
mif-class
The "mif" class
pmcmc-methods
Methods of the "pmcmc" class
init.state-pomp
Return a matrix of initial conditions given a vector of parameters and an initial time.
nlf
Fit Model to Data Using Nonlinear Forecasting (NLF)
pfilter
Particle filter
dprocess-pomp
Evaluate the probability density of state transitions in a Markov process
sir
Seasonal SIR model implemented using two stochastic simulation algorithms.
simulate-pomp
Running simulations of a partially-observed Markov process
spect.pomp-class
The "spect.pomp" and "spect.matched.pomp" classes
traj.match
Trajectory matching
basic.probes
Some probes for partially-observed Markov processes
profile.design
Design matrices for likelihood profile calculations.
dacca
Model of cholera transmission for historic Bengal.
pomp
Partially-observed Markov process object.
sobol
Sobol' low-discrepancy sequence
rprocess-pomp
Simulate the process model of a partially-observed Markov process
ricker
Ricker model with Poisson observations.
rw2
Two-dimensional random-walk process
pomp-package
Partially-observed Markov processes
particles-mif
Generate particles from the user-specified distribution.
dmeasure-pomp
Evaluate the probability density of observations given underlying states in a partially-observed Markov process
probed.pomp-class
The "probed.pomp" and "probe.matched.pomp" classes
plugins
Plug-ins for dynamical models based on stochastic Euler algorithms
verhulst
Simple Verhulst-Pearl (logistic) model.
probed.pomp-methods
Methods of the "probed.pomp", "probe.matched.pomp", "spect.pomp", and "spect.matched.pomp" classes
B-splines
B-spline bases
pomp-class
Partially-observed Markov process class
bsmc
Liu and West Bayesian Particle Filter
Euler-multinomial models
Euler-multinomial models
mif-methods
Methods of the "mif" class
pmcmc-class
The "pmcmc" class
rmeasure-pomp
Simulate the measurement model of a partially-observed Markov process
skeleton-pomp
Evaluate the deterministic skeleton at the given points in state space.
probe
Probe a partially-observed Markov process.
pmcmc
The PMCMC algorithm
pomp-fun
Definition and methods of the "pomp.fun" class
spect
Power spectrum computation for partially-observed Markov processes.
trajectory
Compute trajectories of the determinstic skeleton.
mif
The MIF algorithm
slice.design
Design matrices for likelihood slices.
ou2
Two-dimensional Ornstein-Uhlenbeck process