Package: dynr
Date: 2022-10-16
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 (>= 1.5-0), numDeriv, xtable, latex2exp, grid,
        reshape2, plyr, mice, magrittr, methods, fda, car, stringi,
        tibble, deSolve, Rdpack
Suggests: testthat, roxygen2 (>= 3.1), knitr, rmarkdown, RcppGSL
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%2FRJ-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
Version: 0.1.16-91
Biarch: TRUE
RoxygenNote: 5.0.1
Packaged: 2022-10-16 18:14:37 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: 2022-10-17 07:02:35 UTC
Built: R 4.3.1; x86_64-conda-linux-gnu; 2023-11-06 05:37:30 UTC; unix
