Rdrw-package: Univariate and Multivariate Damped Random Walk Processes
In Rdrw: Univariate and Multivariate Damped Random Walk Processes

Rdrw	R Documentation

Univariate and Multivariate Damped Random Walk Processes

Description

The Rdrw package provides tools for fitting and simulating univariate and multivariate damped random walk processes with optional measurement error standard deviations in a state-space framework. The damped random walk process is also known as an Ornstein–Uhlenbeck process or a first-order continuous-time autoregressive model, CAR(1) or CARMA(1, 0). The package uses Kalman filtering to evaluate the likelihood efficiently, with linear complexity in the number of unique observation times. Polynomial mean coefficients are handled conditionally within the Kalman recursion: they are profiled by generalized least squares for maximum likelihood estimation and sampled from their exact Gaussian conditional distribution for Bayesian inference. The main function drw fits the model and returns maximum likelihood estimates or posterior samples of the model parameters. The function drw.sim simulates time series from univariate or multivariate damped random walk processes.