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Marina Vannucci's Projects

bayesnbmixreg icon bayesnbmixreg

Software for the manuscript Li et al. (2019), "Bayesian negative binomial mixture regression models for the analysis of sequence count and methylation data", Biometrics (to appear).

bios_sglss icon bios_sglss

Code for "Bayesian Image-on-Scalar Regression with a Spatial Global-Local Spike-and-Slab Prior" (BA, 2022+)

bvar_connect icon bvar_connect

Bayesian multi-subject vector autoregressive (VAR) model for inference on effective brain connectivity based on resting-state functional MRI data. Kook et al. (2021), Neuroinformatics, 19, 39-56.

clusteringfunctionaltrajectoriesovertime icon clusteringfunctionaltrajectoriesovertime

Associated files for Liang, M., Koslovsky, M.D., Hebert, E.T., Kendzor, D.E., and Vannucci, M. (2024+) A Bayesian Nonparametric Approach for Clustering Functional Trajectories over Time

dgp-mcem icon dgp-mcem

R code for Monte Carlo EM algorithm of derivative Gaussian process model - Yu et al. (2022, Biometrics)

dgp-theory icon dgp-theory

Code for reproducing results of simulated data sets of sample size 100 in the paper "Semiparametric Bayesian inference for local extrema of functions in the presence of noise"

dmbvs icon dmbvs

R/C code for Bayesian variable selection for Dirichlet-multinomial regression models. Accompany paper: Wadsworth et al. (2016). An Integrative Bayesian Dirichlet-Multinomial Regression Model for the Analysis of Taxonomic Abundances in Microbiome data. BMC Bioinformatics 18:94.

dmlmbvs icon dmlmbvs

A Bayesian model of microbiome data for simultaneous identification of covariate associations and prediction of phenotypic outcomes - Koslovsky et al. (2020), Annals of Applied Stats, 14(3), 1471-1492.

hmmbvs icon hmmbvs

Software for paper Liang, M. et al (2021). Bayesian Continuous-Time Hidden Markov Models with Covariate Selection for Intensive Longitudinal Data with Measurement Error. Psychological Methods, in press.

hncrm icon hncrm

Code for Argiento et al. (2019), "Hierarchical Normalized Completely Random Measures to Cluster Grouped Data", JASA (2019)

linked_precision_matrices icon linked_precision_matrices

MCMC and simulation code for "Bayesian Modeling of Multiple Structural Connectivity Networks During the Progression of Alzheimer’s Disease"

microbvs icon microbvs

Software for the paper Koslovsky, M.D. and Vannucci, M. (2020). MicroBVS: Dirichlet-Tree Multinomial Regression Models with Bayesian Variable Selection – an R package. BMC Bioinformatics, 21:301.

multgraphmodels icon multgraphmodels

Associated Code for Shaddox et al (2018). A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD. Statistics in Biosciences, 10(1), 59-85.

multiggm icon multiggm

Bayesian inference of multiple Gaussian graphical models

multipleplatformbayesiannetworks icon multipleplatformbayesiannetworks

Associated code for Shaddox et al. (2018). Bayesian Inference of Networks Across Multiple Sample Groups and Data Types. Biostatistics, accepted.

npbayes_fmri icon npbayes_fmri

User friendly MATLAB GUI for Bayesian nonparametric spatio-temporal modeling of fMRI data. Accompany paper: Kook et al. (2019). NPBayes-fMRI: Nonparametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data. Statistics in Biosciences, 11(1), 3-21.

pgbvs icon pgbvs

Software for the paper Koslovsky, M.D., Hebert, E.T., Businelle, M.S. and Vannucci, M. (2020). A Bayesian Time-Varying Effect Model for Behavioral mHealth Data. Annals of Applied Statistics, 14(4), 1878-1902.

pibdfc icon pibdfc

This is a repository for the Matlab implementation of the Predictor-Informed Dynamic Functional Connectivity model of Lee et al.

sinc icon sinc

SINC Algorithm from the paper Osborne, N., Peterson, C.B. and Vannucci, M. (2021), "Latent Network Estimation and Variable Selection for Compositional Data via Variational EM", JCGS.

slam icon slam

R code for Monte Carlo EM algorithm of Semiparametric Latent ANOVA Model (SLAM)

snbvbs icon snbvbs

Code for "Scalable Bayesian Variable Selection Regression Models for Count Data", by Miao et al. (2019), in Flexible Bayesian Regression Modelling, Yanan F. et al (Eds), Elsevier, 187-219.

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