A B C D E F I L M N O P R S T V
pomp-package | Partially-observed Markov processes |
as-method | Methods of the "pomp" class |
bspline.basis | B-spline bases |
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 |
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 |
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 |
filter.mean | Methods of the "mif" class |
filter.mean-method | Methods of the "mif" class |
filter.mean-mif | Methods of the "mif" class |
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. |
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 |
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 |
nlf | Fit Model to Data Using Nonlinear Forecasting (NLF) |
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 |
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 |
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 |
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 |
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. |
verhulst | Simple Verhulst-Pearl (logistic) model. |