Continuous Time Structural Equation Modelling
Continuous Time Autocorrelation Function (ctACF)
Calculate Continuous Time Autocorrelation Function (ACF) for Standardi...
Sample more values from an optimized ctstanfit object
Visual model fit diagnostics for ctsem fit objects.
Chi Square test wrapper for ctStanFit objects.
ctCollapse Easily collapse an array margin using a specified function.
ctDeintervalise
ctDensity
Discretise long format continuous time (ctsem) data to specific timest...
Get documentation pdf for ctsem
ctExample2level
Extract samples from a ctStanFit object
ctFit function placeholder
ctFitAuto
ctFitAutoGroupModel
Visual model-fit diagnostics for ctsem fits, optionally split by the m...
Fit and summarise a list of ctsem models
ctGenerate
ctIndplot
Converts absolute times to intervals for wide format ctsem panel data
ctKalman
ctLongToWide Restructures time series / panel data from long format to...
K fold cross validation for ctStanFit objects
Define a ctsem model
Raise the order of a ctsem model object of type 'omx'.
Generate and optionally compile latex equation of subject level ctsem ...
Plots three dimensional y values for quantile plots
Plots uncertainty bands with shading
Create a data.table to compare data generated from a ctsem fit with th...
Create diagnostic plots to assess the goodness-of-fit for a ctsem mode...
ctPredictTIP
Extract Standardized Residuals from a ctsem Fit
ctsem
ctStanContinuousPars
ctStanDiscretePars
ctStanDiscreteParsPlot
ctStanFit
Update a ctStanFit object
Generate data from a ctstanmodel object
Add a $generated object to ctstanfit object, with random data genera...
Get Kalman filter estimates from a ctStanFit object
Convert a frequentist (omx) ctsem model specification to Bayesian (Sta...
ctStanParnames
ctStanPlotPost
Compares model implied density and values to observed, for a ctStanFit...
Extract an array of subject specific parameters from a ctStanFit objec...
Get time independent predictor effect estimates
Update an already compiled and fit ctStanFit object
ctWideNames sets default column names for wide ctsem datasets. Primari...
ctWideToLong Convert ctsem wide to long format
Inverse logit
log1p_exp
plot.ctFitCovCheck
Plots Kalman filter output from ctKalman.
plot.ctStanFit
Prior plotting
Plot an approximate continuous-time ACF object from ctACF
sdcor2cov
Analyse divergences in a stanfit object
Quickly initialise stanfit object from model and data
Convert samples from a stanfit object to the unconstrained scale
Adjust standata from ctsem to only use specific subjects
Optimize / importance sample a stan or ctStan model.
Runs stan, and plots sampling information while sampling.
summary.ctStanFit
Tests if 2 values are close to each other
Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. See <https://osf.io/preprints/psyarxiv/4q9ex_v2> for a detailed tutorial. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>. Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> . <https://cdriver.netlify.app/> contains some tutorial blog posts.