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simStateSpace (version 1.2.15)

Simulate Data from State Space Models

Description

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) .

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Install

install.packages('simStateSpace')

Monthly Downloads

825

Version

1.2.15

License

GPL (>= 3)

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Repository

https://github.com/jeksterslab/simStateSpace

Homepage

https://jeksterslab.github.io/simStateSpace/

Maintainer

Ivan Jacob Agaloos Pesigan

Last Published

February 6th, 2026

Functions in simStateSpace (1.2.15)

Intercept from Steady-State Mean Vector for the Observed Variables in the State Space Model

Simulate Intercept Vectors in a Discrete-Time Vector Autoregressive Model from the Multivariate Normal Distribution

Steady-State Mean Vector for the Latent Variables in the State Space Model

Simulate Diagonal Covariance Matrices from the Multivariate Normal Distribution

SimBetaNCovariate

Simulate Transition Matrices with a Covariate from the Multivariate Normal Distribution

Simulate Covariance Matrices from the Multivariate Normal Distribution

Simulate Transition Matrices from the Multivariate Normal Distribution and Project to Stability

Simulate Transition Matrices from the Multivariate Normal Distribution

Simulate Intercept Vectors in a Continuous-Time Vector Autoregressive Model from the Multivariate Normal Distribution

Steady-State Mean Vector for the Observed Variables in the State Space Model

Simulate Data from a State Space Model (Fixed Parameters)

Simulate Random Drift Matrices from the Multivariate Normal Distribution

Simulate Random Drift Matrices from the Multivariate Normal Distribution and Project to Hurwitz

Simulate Data from a State Space Model (Individual-Varying Parameters)

SimSSMLinSDEIVary

Simulate Data from the Linear Stochastic Differential Equation Model using a State Space Model Parameterization (Individual-Varying Parameters)

SimPhiNCovariate

Simulate Random Drift Matrices with a Covariate from the Multivariate Normal Distribution

SimSSMLinGrowthIVary

Simulate Data from the Linear Growth Curve Model (Individual-Varying Parameters)

SimSSMLinGrowth

Simulate Data from the Linear Growth Curve Model

SimSSMLinSDEFixed

Simulate Data from the Linear Stochastic Differential Equation Model using a State Space Model Parameterization (Fixed Parameters)

Simulate Intercept Vectors in a Discrete-Time Vector Autoregressive Model from the Multivariate Normal Distribution

Test the Drift Matrix

Simulate Data from the Ornstein-Uhlenbeck Model using a State Space Model Parameterization (Individual-Varying Parameters)

SpectralAbscissa

Spectral Abscissa

Test Hurwitz Stability of a Drift Matrix

Simulate Data from the Vector Autoregressive Model (Individual-Varying Parameters)

TestStationarity

Test Stationarity

Simulate Data from the Ornstein-Uhlenbeck Model using a State Space Model Parameterization (Fixed Parameters)

Simulate Data from the Vector Autoregressive Model (Fixed Parameters)

Spectral Radius

simStateSpace-package

simStateSpace: Simulate Data from State Space Models

plot.simstatespace

Plot Method for an Object of Class simstatespace

print.simstatespace

Print Method for an Object of Class simstatespace

as.data.frame.simstatespace

Coerce an Object of Class simstatespace to a Data Frame

as.matrix.simstatespace

Coerce an Object of Class simstatespace to a Matrix

SSMInterceptEta

Intercept from Steady-State Mean Vector for the Latent Variables in the State Space Model

Steady-State Covariance Matrix for the Observed Variables in the State Space Model

Steady-State Covariance Matrix for the Latent Variables in the State Space Model

ProjectToStability

Project Matrix to Stability

Steady-State Covariance Matrix for the Observed Variables in the Linear Stochastic Differential Equation Model

Steady-State Mean Vector for the Observed Variables in the Linear Stochastic Differential Equation Model

Steady-State Mean Vector for the Latent Variables in the Linear Stochastic Differential Equation Model

ProjectToHurwitz

Project Matrix to Hurwitz Stability

Convert Parameters from the Linear Stochastic Differential Equation Model to State Space Model Parameterization

Steady-State Covariance Matrix for the Latent Variables in the Linear Stochastic Differential Equation Model