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pomp (version 0.22-6)
Statistical inference for partially observed Markov processes
Description
Inference methods for partially-observed Markov processes
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Install
install.packages('pomp')
Monthly Downloads
2,108
Version
0.22-6
License
GPL (>= 2)
Maintainer
Aaron King
Last Published
March 13th, 2009
Functions in pomp (0.22-6)
Search all functions
mif-methods
Methods of the "mif" class
sobol
Sobol' low-discrepancy sequence
pfilter
Particle filter
dprocess-pomp
Evaluate the probability density of state transitions in a Markov process
simulate-pomp
Running simulations of a partially-observed Markov process
rprocess-pomp
Simulate the process model of a partially-observed Markov process
euler
Dynamical models based on stochastic Euler algorithms
pomp
Partially-observed Markov process object.
ou2
Two-dimensional Ornstein-Uhlenbeck process
init.state-pomp
Return a matrix of initial conditions given a vector of parameters and an initial time.
rmeasure-pomp
Simulate the measurement model of a partially-observed Markov process
dmeasure-pomp
Evaluate the probability density of observations given underlying states in a partially-observed Markov process
pomp-package
Partially-observed Markov processes
pomp-methods
Methods of the "pomp" class
mif
The MIF algorithm
pomp-class
Partially-observed Markov process
trajectory-pomp
Compute trajectories of the determinstic skeleton.
B-splines
B-spline bases
particles-mif
Generate particles from the user-specified distribution.
Euler-multinomial models
Euler-multinomial models
mif-class
The "mif" class
nlf
Fit Model to Data Using Nonlinear Forecasting (NLF)
skeleton-pomp
Evaluate the deterministic skeleton at the given points in state space.