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dynr
View on CRAN: Click
here
Download and install dynr package within the R console
Install from CRAN:
install.packages("dynr")
Install from Github:
library("remotes")
install_github("cran/dynr")
Install by package version:
library("remotes")
install_version("dynr", "0.1.16-105")
Attach the package and use:
library("dynr")
Maintained by
Michael D. Hunter
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-06-09
Latest Update: 2023-11-28
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) provided a detailed introduction to the
interface and more information on the algorithms.
How to cite:
Michael D. Hunter (2016). dynr: Dynamic Models with Regime-Switching. R package version 0.1.16-105, https://cran.r-project.org/web/packages/dynr
Previous versions and publish date:
0.1.7 (2016-06-09 20:41), 0.1.8-17 (2017-01-09 09:49), 0.1.9-20 (2017-02-24 08:31), 0.1.10-10 (2017-05-21 10:04), 0.1.11-2 (2017-06-17 02:05), 0.1.11-8 (2017-08-21 10:30), 0.1.12-5 (2018-02-08 22:36), 0.1.13-2 (2018-09-15 07:10), 0.1.13-3 (2018-09-16 18:20), 0.1.13-4 (2018-09-24 20:30), 0.1.14-9 (2019-04-02 09:50), 0.1.14-85 (2019-09-13 00:40), 0.1.15-1 (2019-10-05 08:50), 0.1.15-25 (2020-02-11 20:10), 0.1.15-95 (2021-02-20 09:00), 0.1.16-2 (2021-03-16 13:40), 0.1.16-27 (2021-11-18 14:50), 0.1.16-91 (2022-10-17 09:02)
Other packages that cited dynr R package
View dynr citation profile
Other R packages that dynr depends,
imports, suggests or enhances
Functions, R codes and Examples using
the dynr R package
Some associated functions: EMG . EMGsim . ExpandRandomAsLVModel . LinearOsc . LogisticSetPointSDE . NonlinearDFAsim . Oscillator . Outliers . PFAsim . PPsim . RSPPsim . TrueInit_Y14 . VARsim . autoplot.dynrTaste . coef.dynrCook . confint.dynrCook . diag-character-method . dynr-package . dynr.config . dynr.cook . dynr.data . dynr.flowField . dynr.ggplot . dynr.ldl . dynr.mi . dynr.model . dynr.plotFreq . dynr.taste . dynr.taste2 . dynr.trajectory . dynr.version . dynrCook-class . dynrDynamics-class . dynrInitial-class . dynrMeasurement-class . dynrModel-class . dynrNoise-class . dynrRecipe-class . dynrRegimes-class . dynrTrans-class . getdx . internalModelPrep . logLik.dynrCook . names-dynrCook-method . names-dynrModel-method . nobs.dynrCook . nobs.dynrModel . oscData . plot.dynrCook . plotFormula . plotGCV . predict.dynrModel . prep.formulaDynamics . prep.initial . prep.loadings . prep.matrixDynamics . prep.measurement . prep.noise . prep.regimes . prep.tfun . printex . summary.dynrCook . theta_plot . vcov.dynrCook . vdpData .
Some associated R codes: dataDoc.R . dynrCook.R . dynrData.R . dynrFuncAddress.R . dynrGetDerivs.R . dynrMi.R . dynrModel.R . dynrModelInternal.R . dynrPlot.R . dynrPredict.R . dynrRecipe.R . dynrTaste.R . dynrVersion.R . Full dynr package functions and examples
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