Package: simStateSpace 1.2.16

Ivan Jacob Agaloos Pesigan

simStateSpace: Simulate Data from State Space Models

Provides a streamlined and user-friendly framework for simulating data in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. This package was designed to generate data for the simulations performed in Pesigan, Russell, and Chow (2025) <doi:10.1037/met0000779>.

Authors:Ivan Jacob Agaloos Pesigan [aut, cre, cph], Michael A. Russell [ctb], Sy-Miin Chow [ctb]

simStateSpace_1.2.16.tar.gz
simStateSpace_1.2.16.tar.gz(r-4.7-arm64)simStateSpace_1.2.16.tar.gz(r-4.7-x86_64)simStateSpace_1.2.16.tar.gz(r-4.6-arm64)simStateSpace_1.2.16.tar.gz(r-4.6-x86_64)
simStateSpace_1.2.16.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
simStateSpace/json (API)
NEWS

# Install 'simStateSpace' in R:
install.packages('simStateSpace', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jeksterslab/simstatespace/issues

Pkgdown/docs site:https://jeksterslab.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

3.57 score 4 packages 62 scripts 838 downloads 44 exports 2 dependencies

Last updated from:39b1ddd6ad. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK168
linux-devel-x86_64OK172
source / vignettesOK275
linux-release-arm64OK164
linux-release-x86_64OK179
wasm-releaseOK158

Exports:LinSDE2SSMLinSDECovEtaLinSDECovYLinSDEInterceptEtaLinSDEInterceptYLinSDEMeanEtaLinSDEMeanYProjectToHurwitzProjectToStabilitySimAlphaNSimBetaNSimBetaN2SimBetaNCovariateSimCovDiagNSimCovNSimIotaNSimMuNSimMVNSimNuNSimPhiNSimPhiN2SimPhiNCovariateSimSSMFixedSimSSMIVarySimSSMLinGrowthSimSSMLinGrowthIVarySimSSMLinSDEFixedSimSSMLinSDEIVarySimSSMOUFixedSimSSMOUIVarySimSSMVARFixedSimSSMVARIVarySpectralAbscissaSpectralRadiusSSMCovEtaSSMCovYSSMInterceptEtaSSMInterceptYSSMMeanEtaSSMMeanYTestPhiTestPhiHurwitzTestStabilityTestStationarity

Dependencies:RcppRcppArmadillo

Readme and manuals

Help Manual

Help pageTopics
Coerce an Object of Class 'simstatespace' to a Data Frameas.data.frame.simstatespace
Coerce an Object of Class 'simstatespace' to a Matrixas.matrix.simstatespace
Convert Parameters from the Linear Stochastic Differential Equation Model to State Space Model ParameterizationLinSDE2SSM
Steady-State Covariance Matrix for the Latent Variables in the Linear Stochastic Differential Equation ModelLinSDECovEta
Steady-State Covariance Matrix for the Observed Variables in the Linear Stochastic Differential Equation ModelLinSDECovY
Intercept from Steady-State Mean Vector for the Latent Variables in the Linear Stochastic Differential Equation ModelLinSDEInterceptEta
Intercept from Steady-State Mean Vector for the Observed Variables in the Linear Stochastic Differential Equation ModelLinSDEInterceptY
Steady-State Mean Vector for the Latent Variables in the Linear Stochastic Differential Equation ModelLinSDEMeanEta
Steady-State Mean Vector for the Observed Variables in the Linear Stochastic Differential Equation ModelLinSDEMeanY
Plot Method for an Object of Class 'simstatespace'plot.simstatespace
Print Method for an Object of Class 'simstatespace'print.simstatespace
Project Matrix to Hurwitz StabilityProjectToHurwitz
Project Matrix to StabilityProjectToStability
Simulate Intercept Vectors in a Discrete-Time Vector Autoregressive Model from the Multivariate Normal DistributionSimAlphaN
Simulate Transition Matrices from the Multivariate Normal DistributionSimBetaN
Simulate Transition Matrices from the Multivariate Normal Distribution and Project to StabilitySimBetaN2
Simulate Transition Matrices with a Covariate from the Multivariate Normal DistributionSimBetaNCovariate
Simulate Diagonal Covariance Matrices from the Multivariate Normal DistributionSimCovDiagN
Simulate Covariance Matrices from the Multivariate Normal DistributionSimCovN
Simulate Intercept Vectors in a Continuous-Time Vector Autoregressive Model from the Multivariate Normal DistributionSimIotaN
Simulate Set Point Vectors from the Multivariate Normal DistributionSimMuN
Simulate Vectors from the Multivariate Normal DistributionSimMVN
Simulate Intercept Vectors in a Discrete-Time Vector Autoregressive Model from the Multivariate Normal DistributionSimNuN
Simulate Random Drift Matrices from the Multivariate Normal DistributionSimPhiN
Simulate Random Drift Matrices from the Multivariate Normal Distribution and Project to HurwitzSimPhiN2
Simulate Random Drift Matrices with a Covariate from the Multivariate Normal DistributionSimPhiNCovariate
Simulate Data from a State Space Model (Fixed Parameters)SimSSMFixed
Simulate Data from a State Space Model (Individual-Varying Parameters)SimSSMIVary
Simulate Data from the Linear Growth Curve ModelSimSSMLinGrowth
Simulate Data from the Linear Growth Curve Model (Individual-Varying Parameters)SimSSMLinGrowthIVary
Simulate Data from the Linear Stochastic Differential Equation Model using a State Space Model Parameterization (Fixed Parameters)SimSSMLinSDEFixed
Simulate Data from the Linear Stochastic Differential Equation Model using a State Space Model Parameterization (Individual-Varying Parameters)SimSSMLinSDEIVary
Simulate Data from the Ornstein-Uhlenbeck Model using a State Space Model Parameterization (Fixed Parameters)SimSSMOUFixed
Simulate Data from the Ornstein-Uhlenbeck Model using a State Space Model Parameterization (Individual-Varying Parameters)SimSSMOUIVary
Simulate Data from the Vector Autoregressive Model (Fixed Parameters)SimSSMVARFixed
Simulate Data from the Vector Autoregressive Model (Individual-Varying Parameters)SimSSMVARIVary
Spectral AbscissaSpectralAbscissa
Spectral RadiusSpectralRadius
Steady-State Covariance Matrix for the Latent Variables in the State Space ModelSSMCovEta
Steady-State Covariance Matrix for the Observed Variables in the State Space ModelSSMCovY
Intercept from Steady-State Mean Vector for the Latent Variables in the State Space ModelSSMInterceptEta
Intercept from Steady-State Mean Vector for the Observed Variables in the State Space ModelSSMInterceptY
Steady-State Mean Vector for the Latent Variables in the State Space ModelSSMMeanEta
Steady-State Mean Vector for the Observed Variables in the State Space ModelSSMMeanY
Test the Drift MatrixTestPhi
Test Hurwitz Stability of a Drift MatrixTestPhiHurwitz
Test StabilityTestStability
Test StationarityTestStationarity