Dipankar Bandyopadhyay's Projects
R code for simulation experiments in the additive hazards model for bivariate current status data
SAS macro Beta_Regression implements 0-1 augmented, 0-augmented, 1-augmented, and non-augmented lemon-squeezer beta regression models, as described in the paper: Lewis B, Bandyopadhyay D, DeSantis SM and John MT. (2017). Augmented beta regression for periodontal proportion data via the SAS NLMIXED procedure, Journal of Applied Probability and Statistics, 12(1), 49-66
Bayesian Repulsive Bi-clustering, with applications to Periodontal Data
Repository for class materials for EEB 610: Intro to Bayesian Modeling with Stan
Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals
Code for "Bayesian Policy Search for Periodontal Recall Intervals" by Guan et al
Code to implement a Bayesian monotone single-index modeling of spatially-referenced multistate current status data
Programs for computational enhancements in bridged survival regression.
Bayesian Skewed Tensor Normal Modeling
Bayesian Skewed Tensor-t
Bayesian Regression for Skew-t distributed tensor outcomes
cenROCR Package for the estimation of the time-dependent ROC curve and its AUC for censored survival data.
What the Package Does (One Line, Title Case)
Shrinkage priors for variable selection for Bayesian Dirichlet-Multinomial regression model
Lag time between state-level policy interventions and changepoints in COVID-19 outcomes in the United States
Cox model for interval censored data
Weighted least-squares regression with competing risks data
Decisions Optimized in Continuous Time
R package for two dimensional marginal screening for ultra-high dimensional right-censored survival data
It contains the code used for the research: "Embedded Likelihood Function based Estimation under Accelerated Failure Time Models for Clustered Censored Data"
R/JAGS implementation of the cross-domain latent growth curve modeling from "Association between body fat and body mass index from incomplete longitudinal proportion data: Findings from the Fels study" by Tong, Kim, Bandyopadhyay and Sun
R code to conduct gene-screening in ultra high-dimensional survival (right-censored) outcomes
Geostatistical Modeling of Positive Definite Matrices, with Applications to Diffusion Tensor Imaging
R codes to run the shared spatial model for multivariate extreme-valued binary data with non-random missingness
R code to fit a h-likelihood based GEV model to clustered right-censored survival data, under heavy censoring
R package ipcwQR to implement an inverse-probability censoring weighted (IPCW) procedure for censored quantile regression, for (cluster-correlated) partially interval-censored data. Includes both double-censoring, and partially interval-censoring.