Package: dynr
Date: 2021-11-16
Title: Dynamic Models with Regime-Switching
Authors@R: c(person("Lu", "Ou", role="aut"),
    person(c("Michael", "D."), "Hunter", role=c("aut", "cre"), email="mike.dynr@gmail.com", comment=c(ORCID = "0000-0002-3651-6709")),
    person("Sy-Miin", "Chow", role="aut", comment=c(ORCID = "0000-0003-1938-027X")),
	person("Linying", "Ji", role="aut", email=""),
	person("Meng", "Chen", role="aut", email=""),
	person("Hui-Ju", "Hung", role="aut", email=""),
	person("Jungmin", "Lee", role="aut", email="leejapply@gmail.com"),
	person("Yanling", "Li", role="aut", email=""),
	person("Jonathan", "Park", role="aut", email=""),
	person("Massachusetts Institute of Technology", role="cph"),
	person("S. G.", "Johnson", role="cph"),
	person("Benoit", "Scherrer", role="cph"),
	person("Dieter", "Kraft", role="cph"))
Maintainer: Michael D. Hunter <mike.dynr@gmail.com>
URL: https://dynrr.github.io/, https://github.com/mhunter1/dynr
Contact: <dynr@googlegroups.com>
Depends: R (>= 3.0.0), ggplot2
Imports: MASS, Matrix, numDeriv, xtable, latex2exp, grid, reshape2,
        plyr, mice, magrittr, Rdpack, methods, fda, car, stringi,
        tibble, deSolve
Suggests: testthat, roxygen2 (>= 3.1), knitr, rmarkdown
VignetteBuilder: knitr
Description: Intensive longitudinal data have become increasingly prevalent in
    various scientific disciplines. Many such data sets are noisy, multivariate,
    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/RJ-2019-012> provided a detailed introduction to the
    interface and more information on the algorithms.
SystemRequirements: GNU make
NeedsCompilation: yes
License: GPL-3
LazyLoad: yes
LazyData: yes
Collate: 'dynrData.R' 'dynrRecipe.R' 'dynrModelInternal.R'
        'dynrModel.R' 'dynrCook.R' 'dynrPlot.R' 'dynrFuncAddress.R'
        'dynrMi.R' 'dynrTaste.R' 'dynrVersion.R' 'dataDoc.R'
        'dynrGetDerivs.R' 'dynrPredict.R'
RdMacros: Rdpack
Biarch: true
Version: 0.1.16-27
RoxygenNote: 5.0.1
Packaged: 2021-11-16 16:03:16 UTC; mhunter
Author: Lu Ou [aut],
  Michael D. Hunter [aut, cre] (<https://orcid.org/0000-0002-3651-6709>),
  Sy-Miin Chow [aut] (<https://orcid.org/0000-0003-1938-027X>),
  Linying Ji [aut],
  Meng Chen [aut],
  Hui-Ju Hung [aut],
  Jungmin Lee [aut],
  Yanling Li [aut],
  Jonathan Park [aut],
  Massachusetts Institute of Technology [cph],
  S. G. Johnson [cph],
  Benoit Scherrer [cph],
  Dieter Kraft [cph]
Repository: CRAN
Date/Publication: 2021-11-18 13:50:02 UTC
Built: R 4.0.5; x86_64-w64-mingw32; 2022-04-21 15:52:26 UTC; windows
Archs: i386, x64
