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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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Version

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)

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