Package: dynr
Date: 2017-02-23
Title: Dynamic Modeling in R
Authors@R: c(person("Lu", "Ou", role=c("aut", "cre"), email="lzo114@psu.edu"),
    person(c("Michael", "D."), "Hunter", role="aut"),
    person("Sy-Miin", "Chow", role="aut"))
Author: Lu Ou [aut, cre],
    Michael D. Hunter [aut],
    Sy-Miin Chow [aut]
Maintainer: Lu Ou <lzo114@psu.edu>
Depends: R (>= 3.0.0), methods, ggplot2
Imports: MASS, Matrix, numDeriv, xtable, latex2exp, grid, reshape2,
        plyr
Suggests: testthat, roxygen2 (>= 3.1)
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 estimates.
SystemRequirements: GNU make
NeedsCompilation: yes
License: Apache License (== 2.0)
LazyLoad: yes
LazyData: yes
Collate: 'dynrData.R' 'dynrRecipe.R' 'dynrModelInternal.R'
        'dynrModel.R' 'dynrCook.R' 'dynrPlot.R' 'dynrFuncAddress.R'
        'dynrVersion.R' 'dataDoc.R'
Version: 0.1.9-20
RoxygenNote: 5.0.1
Packaged: 2017-02-24 01:02:17 UTC; admin_lzo114
Repository: CRAN
Date/Publication: 2017-02-24 08:31:49
Built: R 3.2.5; x86_64-w64-mingw32; 2017-04-20 08:22:51 UTC; windows
Archs: x64
