R package ctsem - now with hierarchical continuous time dynamic models via Stan

[Charles Driver]

Hey folks, I put this up on CRAN just a few days ago, some here may be interested. Essentially, hierarchical multivariate carmax (continuous time autoregressive moving average with exogenous variables) models with just a few lines of code.
Feedback or questions on any aspect is welcome.

There is a pre-print of an 'in submission' article on the topic here: https://www.researchgate.net/publication/310747801_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling
A quick intro to the software here: https://www.researchgate.net/publication/310747987_Introduction_to_Hierarchical_Continuous_Time_Dynamic_Modelling_With_ctsem

The super quick intro to the software: data with one subject and 1 time point per row, ctModel, ctStanFit, summary, plot

Abstract from the introduction:
 
 ctsem allows for specification and fitting of a range of continuous and discrete time dynamic models with stochastic system noise. The models may include multiple indicators (dynamic factor analysis), multiple, interrelated, potentially higher order processes, and time dependent (varying within subject) and time independent (not varying within subject) covariates. Classic longitudinal models like latent growth curves and latent change score models are also possible.  Version 1 of ctsem provided structural equation model based functionality by linking to the OpenMx software, allowing mixed effects models (random means but fixed regression and variance parameters) for multiple subjects. For version 2 of the R package ctsem, we include a Bayesian specification and fitting routine that uses the Stan probabilistic programming language, via the rstan package in R. This allows for all parameters of the dynamic model to individually vary, using an estimated population mean and variance, and any time independent covariate effects, as a prior. Frequentist approaches with ctsem are documented in a forthcoming JSS publication (Driver, Voelkle, Oud, in press), and in R vignette form at https://cran.r-project.org/package=ctsem/vignettes/ctsem.pdf , here we provide the basics for getting started with the Bayesian approach included in version 2.

Some of the plots:

[Bob Carpenter]

Nice. Thanks for sharing the details. We love
hearing about this kind of thing.

- Bob

> --
> You received this message because you are subscribed to the Google Groups "Stan users mailing list" group.
> To unsubscribe from this group and stop receiving emails from it, send an email to stan-users+...@googlegroups.com.
> To post to this group, send email to stan-...@googlegroups.com.
> For more options, visit https://groups.google.com/d/optout.