Dynamic structural equation models have become extremely popular for analysis of intensive longitudinal data in the social sciences. One outstanding problem is how to handle nonlinear trends and cycles, and in this paper we propose to do this in a very flexible manner using regression splines. We test the methods in simulation studies, and then illustrate them by analyzing a diary data set on alcohol consumption and stress. Open-source Stan code is available from our OSF repository. PsyArXiv preprint currently under revision.

This paper considers rank and preference modeling for the case in which data arrive sequentially, rather than in a batch. The goal is to compute the posterior distribution incrementally in time, so that it can be quickly updated when new data arrives. To this end, we develop a sequential Monte Carlo algorithm for the Bayesian Mallows model. arXiv preprint currently under revision.

This paper presents the R package “galamm”, which contains open-source implementations for generalized additive latent and mixed models (GALAMMs). Published in Multivariate Behavioral Research.

This paper was motivated by the need to use flexible spline models to model how cognitive abilities change with age, and investigating how changes in abilities in certain cognitive domains can be explained by changes in the brain. To this end we developed a framework called Generalized Additive Latent and Mixed Models (GALAMM), combining generalized additive mixed models, structural equation modeling, and mxied effect modeling. We also propose algorithms for estimating the models, using sparse matrix methods and automatic differentation. Published in Psychometrika.

This paper considers estimation of lifespan brain trajectories from longitudinal data, using generalized additive mixed models. It studies how to distinguish longitudinal effects from cohort effects, and shows example code in R which practitioners can use for their own data. Published in NeuroImage.

This paper was motivated by the need to analyze neuroimaging data from multiple cohorts, when raw data could not be shared. We developed a method for meta analysis in which the results of fitting generalized additive (mixed) models could be shared, and a final meta analytic estimate computed based on weighting the individual fits. Published in NeuroImage.

This paper presents the BayesMallows R package, for analysis of rank and preference data. Published in the R Journal.

This paper considers predicting whether people are right or left brain dominant for language processing, based on their result in dichotic listening experiments as well as their handedness. Published in Laterality

This paper describes the hdme R package, which provides implementation of variable selection in the presence of measurement error. Published in Journal of Open Source Software.

The matrix uncertainty selector is a modification of the Dantzig selector, for the case of variable selection with noisy predictors. In this paper we extend the matrix uncertainty selector to the generalized linear model case, and propose a computationally efficient computational algorithm. Compared to other methods that correct for the effect of measurement error, the matrix uncertainty selector and its extension do not require a precise estimate of the noise variance, which is an advantage in practical use. Published in Journal of Computational and Graphical Statistics.