Software Projects

ctsem: https://github.com/cdriveraus/ctsem
ctsem allows for easy specification and fitting of a range of continuous and discrete time dynamic models, including multiple indicators (dynamic factor analysis), multiple, 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. Continuous and binary data models are available. Version 1 of ctsem provided SEM 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 hierarchical 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. Version 3 (the current state) further allows for state dependencies and non-linearities in the parameter specification (e.g., time varying parameters), and allows for faster approximation approaches to the hierarchical models introduced in version 2.  

The current manual is at https://cran.r-project.org/package=ctsem/vignettes/hierarchicalmanual.pdf.
The original ctsem V1 is documented in a JSS publication (Driver, Voelkle, Oud, 2017), and in R vignette form at https://cran.r-project.org/package=ctsemOMX/vignettes/ctsem.pdf, however these OpenMx based functions have been split off into a sub package, ctsemOMX. For most use cases the newer formulation (with Kalman filtering coded in Stan) is faster, more robust, and more flexible, and both default to maximum likelihood. For cases with many subjects, few time points, and no individual differences in timing, ctsemOMX may be faster.

bigIRT: https://github.com/cdriveraus/bigIRT
bigIRT is an R software package for the estimation of a range of item response theory models in contexts where there are large numbers of responses, students, and items, but the number of responses for individual students may still be low, resulting in high sparsity. The models are for binary data, using uni or multivariate, scales, 1 to 4 parameter logistic models, with covariate moderators of ability and item parameters. Estimation is maximum a posteriori, with optional empirical Bayesian approaches.