Statistical inference for partially observed Markov processes


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Documentation for package ‘pomp’ version 0.28-2

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A B C D E F I L M N O P R S T V

-- --

pomp-package Partially-observed Markov processes

-- A --

as-method Methods of the "pomp" class

-- B --

bspline.basis B-spline bases

-- C --

coef-method Methods of the "pomp" class
coef-pomp Methods of the "pomp" class
coef<- Methods of the "pomp" class
coef<--method Methods of the "pomp" class
coef<--pomp Methods of the "pomp" class
coerce-method Methods of the "pomp" class
compare.mif Methods of the "mif" class
continue The MIF algorithm
continue-method The MIF algorithm
continue-mif The MIF algorithm
conv.rec Methods of the "mif" class
conv.rec-method Methods of the "mif" class
conv.rec-mif Methods of the "mif" class

-- D --

data.array Methods of the "pomp" class
data.array-method Methods of the "pomp" class
data.array-pomp Methods of the "pomp" class
deulermultinom Euler-multinomial models
dmeasure Evaluate the probability density of observations given underlying states in a partially-observed Markov process
dmeasure-method Evaluate the probability density of observations given underlying states in a partially-observed Markov process
dmeasure-pomp Evaluate the probability density of observations given underlying states in a partially-observed Markov process
dprocess Evaluate the probability density of state transitions in a Markov process
dprocess-method Evaluate the probability density of state transitions in a Markov process
dprocess-pomp Evaluate the probability density of state transitions in a Markov process

-- E --

euler Plug-ins for dynamical models based on stochastic Euler algorithms
euler.simulate Plug-ins for dynamical models based on stochastic Euler algorithms
euler.sir Seasonal SIR model implemented as an Euler-multinomial model
eulermultinom Euler-multinomial models

-- F --

filter.mean Methods of the "mif" class
filter.mean-method Methods of the "mif" class
filter.mean-mif Methods of the "mif" class

-- I --

init.state Return a matrix of initial conditions given a vector of parameters and an initial time.
init.state-method Return a matrix of initial conditions given a vector of parameters and an initial time.
init.state-pomp Return a matrix of initial conditions given a vector of parameters and an initial time.

-- L --

logLik-method Methods of the "mif" class
logLik-mif Methods of the "mif" class
LondonYorke Reported cases of chickenpox, measles, and mumps from Baltimore and New York, 1928-1972

-- M --

mif The MIF algorithm
mif-class The "mif" class
mif-method The MIF algorithm
mif-methods Methods of the "mif" class
mif-mif The MIF algorithm
mif-pomp The MIF algorithm

-- N --

nlf Fit Model to Data Using Nonlinear Forecasting (NLF)

-- O --

onestep.density Plug-ins for dynamical models based on stochastic Euler algorithms
onestep.simulate Plug-ins for dynamical models based on stochastic Euler algorithms
ou2 Two-dimensional Ornstein-Uhlenbeck process

-- P --

particles Generate particles from the user-specified distribution.
particles-method Generate particles from the user-specified distribution.
particles-mif Generate particles from the user-specified distribution.
periodic.bspline.basis B-spline bases
pfilter Particle filter
pfilter-method Particle filter
pfilter-mif Particle filter
pfilter-pomp Particle filter
plot-method Methods of the "mif" class
plot-method Methods of the "pomp" class
plot-mif Methods of the "mif" class
plot-pomp Methods of the "pomp" class
pomp Partially-observed Markov process object.
pomp-class Partially-observed Markov process
pomp-methods Methods of the "pomp" class
pred.mean Methods of the "mif" class
pred.mean-method Methods of the "mif" class
pred.mean-mif Methods of the "mif" class
pred.var Methods of the "mif" class
pred.var-method Methods of the "mif" class
pred.var-mif Methods of the "mif" class
print-method Methods of the "pomp" class
print-pomp Methods of the "pomp" class
profile.design Design matrices for likelihood slices and profiles

-- R --

reulermultinom Euler-multinomial models
rmeasure Simulate the measurement model of a partially-observed Markov process
rmeasure-method Simulate the measurement model of a partially-observed Markov process
rmeasure-pomp Simulate the measurement model of a partially-observed Markov process
rprocess Simulate the process model of a partially-observed Markov process
rprocess-method Simulate the process model of a partially-observed Markov process
rprocess-pomp Simulate the process model of a partially-observed Markov process
rw2 Two-dimensional random-walk process

-- S --

show-method Methods of the "pomp" class
show-pomp Methods of the "pomp" class
simulate-method Running simulations of a partially-observed Markov process
simulate-pomp Running simulations of a partially-observed Markov process
skeleton Evaluate the deterministic skeleton at the given points in state space.
skeleton-method Evaluate the deterministic skeleton at the given points in state space.
skeleton-pomp Evaluate the deterministic skeleton at the given points in state space.
slice.design Design matrices for likelihood slices and profiles
sobol Sobol' low-discrepancy sequence
states Methods of the "pomp" class
states-method Methods of the "pomp" class
states-pomp Methods of the "pomp" class

-- T --

time-method Methods of the "pomp" class
time-pomp Methods of the "pomp" class
time<- Methods of the "pomp" class
time<--method Methods of the "pomp" class
time<--pomp Methods of the "pomp" class
traj.match Trajectory matching
trajectory Compute trajectories of the determinstic skeleton.
trajectory-method Compute trajectories of the determinstic skeleton.
trajectory-pomp Compute trajectories of the determinstic skeleton.

-- V --

verhulst Simple Verhulst-Pearl (logistic) model.