cran
. See also theR-universe documentation.Package: dynr 0.1.16-105
Michael D. Hunter
dynr: Dynamic Models with Regime-Switching
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.
Authors:
dynr_0.1.16-105.tar.gz
dynr_0.1.16-105.tar.gz(r-4.5-noble)dynr_0.1.16-105.tar.gz(r-4.4-noble)
dynr_0.1.16-105.tgz(r-4.4-emscripten)dynr_0.1.16-105.tgz(r-4.3-emscripten)
dynr.pdf |dynr.html✨
dynr/json (API)
NEWS
# Install 'dynr' in R: |
install.packages('dynr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mhunter1/dynr/issues
- EMG - Single-subject time series of facial electromyography data
- EMGsim - Simulated single-subject time series to capture features of facial electromyography data
- LinearOsc - Simulated time series data for a deterministic linear damped oscillator model
- LogisticSetPointSDE - Simulated time series data for a stochastic linear damped oscillator model with logistic time-varying setpoints
- NonlinearDFAsim - Simulated multi-subject time series based on a dynamic factor analysis model with nonlinear relations at the latent level
- Oscillator - Simulated time series data of a damped linear oscillator
- Outliers - Simulated time series data for detecting outliers.
- PFAsim - Simulated time series data of a multisubject process factor analysis
- PPsim - Simulated time series data for multiple eco-systems based on a predator-and-prey model
- RSPPsim - Simulated time series data for multiple eco-systems based on a regime-switching predator-and-prey model
- TrueInit_Y14 - Simulated multilevel multi-subject time series of a Van der Pol Oscillator
- VARsim - Simulated time series data for multiple imputation in dynamic modeling.
- oscData - Another simulated multilevel multi-subject time series of a damped oscillator model
- vdpData - Another simulated multilevel multi-subject time series of a Van der Pol Oscillator
Last updated 1 years agofrom:96dda34c0d. Checks:OK: 1 NOTE: 1. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 25 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 25 2024 |
Exports:coef<-diagdynr.configdynr.cookdynr.datadynr.flowFielddynr.ggplotdynr.ldldynr.midynr.modeldynr.plotFreqdynr.tastedynr.taste2dynr.trajectorydynr.versiongetdxplotFormulaplotGCVprep.formulaDynamicsprep.initialprep.loadingsprep.matrixDynamicsprep.measurementprep.noiseprep.regimesprep.tfunprintprintexshowtheta_plot
Dependencies:abindashbackportsbitbit64bitopsbootbroomcarcarDataclicliprclustercodetoolscolorspacecowplotcpp11crayonDerivdeSolvedoBydplyrfansifarverfdafdsFNNforcatsforeachFormulagenericsggplot2glmnetgluegtablehavenhdrcdehmsisobanditeratorsjomokernlabKernSmoothkslabelinglatex2explatticelifecyclelme4locfitmagrittrMASSMatrixMatrixModelsmclustmgcvmicemicrobenchmarkminqamitmlmodelrmulticoolmunsellmvtnormnlmenloptrnnetnumDerivordinalpanpbkrtestpcaPPpillarpkgconfigplyrpracmaprettyunitsprogresspurrrquantregR6rainbowrbibutilsRColorBrewerRcppRcppEigenRCurlRdpackreadrreshape2rlangrpartscalesshapeSparseMstringistringrsurvivaltibbletidyrtidyselecttzdbucminfutf8vctrsviridisLitevroomwithrxtable
Example: A Linear Stochastic Differential Equation Model
Rendered fromlinearSDE.Rmd
usingknitr::rmarkdown
on Nov 25 2024.Last update: 2022-10-17
Started: 2018-09-15
Installation for Developers
Rendered fromInstallationForDevelopers.Rnw
usingutils::Sweave
on Nov 25 2024.Last update: 2023-11-29
Started: 2018-09-15
Installation for Users
Rendered fromInstallationForUsers.Rnw
usingutils::Sweave
on Nov 25 2024.Last update: 2023-11-29
Started: 2017-05-21
Linear discrete-time regime-switching models
Rendered fromLinearDiscreteTimeModels.Rnw
usingutils::Sweave
on Nov 25 2024.Last update: 2022-10-17
Started: 2018-09-15
Nonlinear continuous-time models
Rendered fromNonlinearContinuousTimeModels.Rnw
usingutils::Sweave
on Nov 25 2024.Last update: 2019-09-12
Started: 2018-09-15