pomp5.11 package

Statistical Inference for Partially Observed Markov Processes

Approximate Bayesian computation

accumulator variables

Coerce to data frame

as.pomp

Tools for reproducible computations

Basic POMP model components.

Useful probes for partially-observed Markov processes

Beta-binomial distribution

Nicholson's blowflies.

The Liu and West Bayesian particle filter

B-spline bases

Historical childhood disease incidence data

Extract, set, or alter coefficients

Concatenate

Concatenate

Conditional log likelihood

Continue an iterative calculation

Covariates

Estimate a covariance matrix from algorithm traces

C snippets

Model of cholera transmission for historic Bengal.

Design matrices for pomp calculations

dinit specification

dinit workhorse

dmeasure specification

dmeasure workhorse

dprior workhorse

dprocess specification

dprocess workhorse

Ebola outbreak, West Africa, 2014-2016

Effective sample size

Elementary computations on POMP models.

emeasure specification

emeasure workhorse

Parameter estimation algorithms for POMP models.

Eulermultinomial and gamma-whitenoise distributions

Filtering mean

Filtering trajectories

flow workhorse

Forecast mean

Gompertz model with log-normal observations.

Hitching C snippets and R functions to pomp_fun objects

Ensemble Kalman filters

Kalman filter

listie

Loading and unloading shared-object libraries

Log likelihood

The log-mean-exp trick

Lookup table

Monte Carlo adjusted profile

Melt

Iterated filtering: maximum likelihood by iterated, perturbed Bayes ma...

Nonlinear forecasting

Objective functions

obs

Two-dimensional discrete-time Ornstein-Uhlenbeck process

parameter transformations

Create a matrix of parameters

partrans workhorse

Parus major population dynamics

Particle filter

pomp plotting facilities

The particle Markov chain Metropolis-Hastings algorithm

The basic pomp class

pre-built pomp examples

The "pomp_fun" class

Inference for partially observed Markov processes

Constructor of the basic pomp object

Prediction mean

Prediction variance

Print methods

prior specification

Probe matching

Probes (AKA summary statistics)

MCMC proposal distributions

pStop, pWarn, pMess

Resample

Ricker model with Poisson observations.

rinit specification

rinit workhorse

rmeasure specification

rmeasure workhorse

rprior workhorse

rprocess specification

rprocess workhorse

rw_sd

Two-dimensional random-walk process

Simulated annealing with box constraints.

Saved states

Show methods

Simulations of a partially-observed Markov process

Compartmental epidemiological models

skeleton specification

skeleton workhorse

Spectrum matching

Power spectrum

Spy

Latent states

Summary methods

Methods to extract and manipulate the obseration times

The zero time

Traces

Trajectory matching

Trajectory of a deterministic model

Transformations

Undefined

Facilities for making additional information to basic components

Verhulst-Pearl model

vmeasure specification

vmeasure workhorse

Window

Workhorse functions for the `pomp`

algorithms.

Weighted particle filter

Weighted quantile function

Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.

Maintainer: Aaron A. King License: GPL-3 Last published: 2024-09-12