person(c("Michael", "D."), "Hunter", role=c("aut", "cre"), email="dynr.team@gmail.com", comment=c(ORCID = "0000-0002-3651-6709")),
Maintainer: Michael D. Hunter <dynr.team@gmail.com>

Title: Dynamic Models with Regime-Switching
person(c("Michael", "D."), "Hunter", role=c("aut", "cre"), email="mike.dynr@gmail.com", comment=c(ORCID = "0000-0002-3651-6709")),
Maintainer: Michael D. Hunter <mike.dynr@gmail.com>
subject time series data or multiple-subject longitudinal data. Ou, Hunter, &
Chow (2019) <doi:10.32614/RJ-2019-012> provided a detailed introduction to the
interface and more information on the algorithms.

Imports: MASS, Matrix (>= 1.5-0), numDeriv, xtable, latex2exp, grid,
reshape2, plyr, mice, magrittr, methods, fda, car, stringi,
Suggests: testthat, roxygen2 (>= 3.1), knitr, rmarkdown, RcppGSL
and multi-subject in nature. The change functions may also be continuous,
or continuous but interspersed with periods of discontinuities (i.e.,
showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an
R package that implements a set of computationally efficient algorithms for
handling a broad class of linear and nonlinear discrete- and continuous-
time models with regime-switching properties under the constraint of
linear Gaussian measurement functions. The discrete-time models can
generally take on the form of a state-space or difference equation model.
The continuous-time models are generally expressed as a set of ordinary
or stochastic differential equations. All estimation and computations
are performed in C, but users are provided with the option to specify the
model of interest via a set of simple and easy-to-learn model specification
functions in R. Model fitting can be performed using single-subject time
series data or multiple-subject longitudinal data. Ou, Hunter, & Chow
(2019) <doi:10.32614%2FRJ-2019-012> provided a detailed introduction to the

Authors@R: c(person("Lu", "Ou", role=c("aut", "cre"), email="lzo114@psu.edu"),
Imports: numDeriv, xtable, latex2exp, grid, reshape2, plyr
Description: 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