
A dataset obtained and analyzed in Yang and Chow (2010).


datasets

Dynamic modeling of all kinds in R. These include models of
processes in discrete time or continuous time. They also include processes
that are linear or nonlinear. Latent variables can be continuous (e.g. state
space models) or discrete (e.g. regime-switching models). The general approach
involves maximum likelihood estimation of single- and multi-subject models of
latent time series with the extended Kalman filter and Kim filter. The user
provides recipes and data which are combined into a model that is then cooked to
obtain free parameter estimates.

Lu Ou

dynr

Dynamic Modeling in R

EMG function

A data frame with 695 rows and 4 variables

EMG: Single-subject time series of facial electromyography data

Description

Usage

Arguments

Format

Details