marinavannucci Goto Github PK
Name: Marina Vannucci
Type: User
Company: Rice University
Bio: Noah Harding Professor of Statistics, Rice University
Location: Houston, TX, USA
Name: Marina Vannucci
Type: User
Company: Rice University
Bio: Noah Harding Professor of Statistics, Rice University
Location: Houston, TX, USA
Software for the paper Cassese et al. (2019), "Bayesian Nonparametric Spiked-Process Prior for Dynamic Model Selection", Bayesian Analysis, 14(2),553-572.
Functions to implement Bayesian Tensor Modeling (BT-SVM and BT-LR) in R
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).
Random Phase-Amplitude Gaussian Process
Code for "Bayesian Image-on-Scalar Regression with a Spatial Global-Local Spike-and-Slab Prior" (BA, 2022+)
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.
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
R code for Monte Carlo EM algorithm of derivative Gaussian process model - Yu et al. (2022, Biometrics)
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"
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.
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.
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.
Code for Argiento et al. (2019), "Hierarchical Normalized Completely Random Measures to Cluster Grouped Data", JASA (2019)
MCMC and simulation code for "Bayesian Modeling of Multiple Structural Connectivity Networks During the Progression of Alzheimer’s Disease"
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.
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.
Bayesian inference of multiple Gaussian graphical models
Associated code for Shaddox et al. (2018). Bayesian Inference of Networks Across Multiple Sample Groups and Data Types. Biostatistics, accepted.
Bayesian Multivariate HMM
This code implements a negative binomial dynamic linear model for count data. To access code: Please submit requests to [email protected].
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.
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.
This is a repository for the Matlab implementation of the Predictor-Informed Dynamic Functional Connectivity model of Lee et al.
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.
R code for Monte Carlo EM algorithm of Semiparametric Latent ANOVA Model (SLAM)
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.
sparse VAR approximate HSMM
Statistical Methods in Epilepsy
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