Methodology paper accepted in Psychological Methods

Bayesian Continuous-Time Hidden Markov Models with Covariate Selection for Intensive Longitudinal Data with Measurement Error

Continuous-time hidden Markov models enable researchers to study the relations between risk factors and outcomes repeatedly measured at unbalanced, unequally spaced assessment times while accommodating measurement error. Despite extensive implementation of variable selection methods designed to identify potential relations in exploratory settings for hypothesis generation, currently none have been applied... [Read More]
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Methodology paper accepted at Annals of Applied Statistics

A Bayesian Time-Varying Effect Model for Behavioral mHealth Data

The integration of mobile health (mHealth) devices into behavioral health research has fundamentally changed the way researchers and interventionalists are able to collect data as well as deploy and evaluate intervention strategies. In these studies, researchers often collect intensive longitudinal data (ILD) using ecological momentary assessment methods, which aim to... [Read More]

Methodology paper accepted at Annals of Applied Statistics

A Bayesian model of microbiome data for simultaneous identification of covariate association and prediction of phenotypic outcomes

One of the major research questions regarding human microbiome studies is the feasibility of designing interventions that modulate the composition of the microbiome to promote health and cure disease. This requires extensive understanding of the modulating factors of the microbiome, such as dietary intake, as well as the relation between... [Read More]

Software paper accepted in BMC Bioinoformatics

MicroBVS - Dirichlet-tree multinomial regression models with Bayesian variable selection - an R package

We present MicroBVS, an R package for Dirichlet-tree multinomial models with Bayesian variable selection, for the identification of covariates associated with microbial taxa abundance data. The underlying Bayesian model accommodates phylogenetic structure in the abundance data and various parameterizations of covariates’ prior probabilities of inclusion. While developed to study the... [Read More]