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pomp (version 0.21-3)
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
2,109
Version
0.21-3
License
GPL (>= 2)
Maintainer
Aaron King
Last Published
February 15th, 2017
Functions in pomp (0.21-3)
Search all functions
init.state-pomp
Return a matrix of initial conditions given a vector of parameters and an initial time.
skeleton-pomp
Evaluate the deterministic skeleton at the given points in state space.
rprocess-pomp
Simulate the process model of a partially-observed Markov process
mif-methods
Methods of the "mif" class
pomp-methods
Methods of the "pomp" class
dmeasure-pomp
Evaluate the probability density of observations given underlying states in a partially-observed Markov process
sobol
Sobol' low-discrepancy sequence
B-splines
B-spline bases
rmeasure-pomp
Simulate the measurement model of a partially-observed Markov process
euler
Dynamical models based on stochastic Euler algorithms
ou2
Two-dimensional Ornstein-Uhlenbeck process
dprocess-pomp
Evaluate the probability density of state transitions in a Markov process
pomp-class
Partially-observed Markov process
Euler-multinomial models
Euler-multinomial models
simulate-pomp
Running simulations of a partially-observed Markov process
trajectory-pomp
Compute trajectories of the determinstic skeleton.
mif
The MIF algorithm
pomp-package
Partially-observed Markov processes
pomp
Partially-observed Markov process object.
particles-mif
Generate particles from the user-specified distribution.
pfilter
Particle filter
mif-class
The "mif" class