News

Methodology paper accepted at Biometrics

A Bayesian Semiparametric Mixture Model for Clustering Zero-Inflated Microbiome Data

Methodology paper accepted at BMC Bioinformatics

Analyzing Microbiome Data with Taxonomic Misclassification using a Zero-Inflated Dirichlet-Multinomial Model

Methods paper accepted in Bayesian Analysis

A Unified Bayesian Framework for Modeling Measurement Error in Multinomial Data

Dr. Koslovsky Wins CNS Teaching and Mentoring Award

Teaching and Mentoring Award

Methods paper accepted at Statistics and Computing

Bayesian Nonparametric Approach for Clustering Functional Trajectories over Time

Awarded CESU Grant

Bayesian Sparse Dirichlet-Multinomial Models for Discovering Latent Structure in High-Dimensional Compositional Count Data

Methodology paper accepted at Bayesian Analysis

Dynamic Functional Variable Selection for Multimodal mHealth Data

Awarded NSF Grant

Bayesian Sparse Dirichlet-Multinomial Models for Discovering Latent Structure in High-Dimensional Compositional Count Data

Methods paper accepted in Bayesian Analysis

Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors

Methods paper accepted in Statistics in Medicine

A Bayesian Joint Model for Mediation Effect Selection in Compositional Microbiome Data

Methodology paper accepted in Biometrics

A Bayesian Zero-Inflated Dirichlet-Multinomial Regression Model for Multivariate Compositional Count Data

Methodology paper accepted in Biometrics

Infinite Hidden Markov Models for Multiple Multivariate Time Series with Missing Data

Methodology paper accepted in Psychological Methods

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

Methodology paper accepted at Annals of Applied Statistics

A Bayesian Time-Varying Effect Model for Behavioral mHealth Data

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

Software paper accepted in BMC Bioinoformatics

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