dynr: Dynamic Modeling in R

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.

Version: 0.1.9-20
Depends: R (≥ 3.0.0), methods, ggplot2
Imports: MASS, Matrix, numDeriv, xtable, latex2exp, grid, reshape2, plyr
Suggests: testthat, roxygen2 (≥ 3.1)
Published: 2017-02-24
Author: Lu Ou [aut, cre], Michael D. Hunter [aut], Sy-Miin Chow [aut]
Maintainer: Lu Ou <lzo114 at psu.edu>
License: Apache License (== 2.0)
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: NEWS
In views: TimeSeries
CRAN checks: dynr results

Downloads:

Reference manual: dynr.pdf
Package source: dynr_0.1.9-20.tar.gz
Windows binaries: r-devel: dynr_0.1.9-20.zip, r-release: dynr_0.1.9-20.zip, r-oldrel: dynr_0.1.9-20.zip
OS X El Capitan binaries: r-release: dynr_0.1.9-20.tgz
OS X Mavericks binaries: r-oldrel: dynr_0.1.9-20.tgz
Old sources: dynr archive

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