Package: cts
Title: Continuous Time Autoregressive Models
Version: 1.0-1
Author: Fortran original by G. Tunnicliffe-Wilson and Zhu Wang, R port by Zhu Wang.
Description: Continuous Time Autoregressive Models and the Kalman Filter
Maintainer: Zhu Wang <zwang@scharp.org>
License: GPL version 2 or later
Packaged: Fri Sep 29 11:59:55 2006; zwang

Author: Fortran original by G. Tunnicliffe-Wilson and Zhu Wang, R port
by Zhu Wang with contribution from John Nash.
Description: Continuous Time Autoregressive Models and the Kalman

by Zhu Wang with contribution from John Nash, Netlib and NAG

Author: Fortran original by G. Tunnicliffe-Wilson and Zhu Wang, R port by Zhu Wang with contribution from John Nash, Netlib and NAG authors

Description: Functions to Fit Continuous Time Autoregressive Models with the Kalman Filter.

Description: Functions to fit continuous time autoregressive models with the Kalman filter.

Description: Functions to fit continuous time autoregressive models with the Kalman filter (Wang (2013) <doi:10.18637/jss.v053.i05>).

Author: Fortran original by G. Tunnicliffe-Wilson and Zhu Wang, R port by Zhu Wang with contribution from John Nash.

Package: cts
Title: Continuous Time Autoregressive Models
Version: 1.0
Author: Fortran original by G. Tunnicliffe-Wilson and Zhu Wang, R port by Zhu Wang.
Description: Continuous Time Autoregressive Models and the Kalman Filter
Maintainer: Zhu Wang <zwang@scharp.org>
License: GPL version 2 or later
Packaged: Thu Jun 22 11:07:29 2006; zwang

Authors@R: c(person("Charles", "Driver", role =
person("Manuel", "Voelkle", role = c("aut","cph")),
person("Han", "Oud", role = c("aut","cph") ))
Description: A multivariate continuous (and discrete) time dynamic modelling
package for panel and time series data, using linear stochastic differential
equations. Contains a faster frequentist set of functions using OpenMx for
single subject and mixed-effects (random intercepts only) structural
equation models, or a hierarchical Bayesian implementation using Stan that
allows for random effects over all model parameters. Allows for modelling of
multiple noisy measurements of multiple stochastic processes, time varying
input / event covariates, and time invariant covariates used to predict the
Imports: rstan, plyr, Matrix, stats, utils, graphics, methods,
Suggests: knitr, testthat, PSM, cts, yuima, shinystan