Software

[Updated: 12/17/2024; * = a student author, when the work was conducted]

• WinBUGS code from Bandyopadhyay D, Reich BJ and Slate E. (2009). Bayesian modeling of multivariate spatial binary data with applications to dental caries, Statistics in Medicine, 28, 3492-3508

• SAS macro from *Lin L, Bandyopadhyay D, Lipsitz S and Sinha D (2010). Association models for clustered data with binary and continuous responses, Biometrics, 66(1), 287-293

• R code from Reich BJ, Bandyopadhyay D. (2010). A latent factor model for spatial data with informative missingness, The Annals of Applied Statistics, 4(1), 439-459

• WinBUGS code from Bandyopadhyay D, Sinha D, Lipsitz S, Letourneau E. (2010). Changing approaches of prosecutors towards juvenile repeated sex-offenders: a Bayesian evaluation, The Annals of Applied Statistics, 4(2), 805-829

• SAS macro from *Li X, Bandyopadhyay D, Lipsitz S and Sinha D. (2011). Likelihood methods for binary responses of present components in a cluster, Biometrics, 67(2), 629-635

• R package 'ordcrm' from
  (i) *VanMeter EM, Garett-Mayer E and Bandyopadhyay D. (2011). Proportional odds model for dose finding clinical trial designs with ordinal toxicity grading, Statistics in Medicine, 30(17), 2070-2080;  &
  (ii) *VanMeter EM, Garett-Mayer E and Bandyopadhyay D. (2012). Dose finding clinical trial design for ordinal toxicity grades using the continuation ratio model: an extension of the continual reassessment method, Clinical Trials, 9(3), 303-313

• R package 'nlsmsn' from Lachos VH, Bandyopadhyay D and *Garay AM. (2011). Heteroscedastic non-linear regression models based on scale mixtures of skew-normal distributions, Statistics and Probability Letters, 81(8), 1208-1217

• R and WinBUGS codes from Lachos VH, Bandyopadhyay D and Dey DK. (2011). Linear and non-linear mixed-effect models for censored HIV viral loads using normal/independent distributions, Biometrics, 67(4), 1594-1604

• WinBUGS code from Bandyopadhyay D, Reich BJ and Slate E. (2011). A spatial Beta-Binomial model for clustered count data on dental caries, Statistical Methods in Medical Research, 20, 85-102.

• R and WinBUGS code from Bandyopadhyay D, Lachos VH, Castro LM and Dey DK. (2012). Skew-normal/independent linear mixed models for censored responses with applications to HIV viral loads, Biometrical Journal, 54(3), 405-425

• WinBUGS code from *Boehm L, Reich BJ and Bandyopadhyay D. (2013). Bridging conditional and marginal shapes for spatially-referenced binary data, Biometrics, 69(2), 545-554

• R code from *Mustvari T, Bandyopadhyay D, Lesaffre E and  Declerck D. (2013). A multilevel model for spatially correlated binary data in the presence of misclassification: An application in oral health research,  Statistics in Medicine, 32(30), 5241-5259

• R code (using MPI) from Reich BJ, Bandyopadhyay D and Bondell H. (2013). A nonparametric spatial model for periodontal data with non-random missingness, Journal of the American Statistical Association, 108, 820-831

• WinBUGS code for the full model from *Galvis DM, Bandyopadhyay D and Lachos VH. (2014). Augmented mixed beta regression models for periodontal proportion data, Statistics in Medicine, 33(21), 3759-3771

• R code from *Schnell P, Bandyopadhyay D, Reich BJ and Nunn M. (2015). A marginal cure-rate proportional hazards model for spatial survival data, Journal of the Royal Statistical Society - Series C, 64(4), 673-691

• R code from *Matos LM, Bandyopadhyay D, Castro LM and Lachos VH. (2015). Influence assessment in censored mixed-effects models using the multivariate Student's-t distribution, Journal of Multivariate Analysis, 141, 104-117     

• R code for MCMC implementation from Bandyopadhyay D and Canale A. (2016). Nonparametric spatial models for clustered ordered periodontal data, Journal of the Royal Statistical Society - Series C, 65(4), 619-640

• R code to implement the fractional-risk-set based (i) weighted log-rank test, and (ii) weighted Kaplan-Meier tests, proposed in:
  (i) Bandyopadhyay D and Datta S (2008). Testing equality of survival distributions when the population marks are missing, Journal of Statistical Planning and Inference, 138, 1722-1732  &
  (ii) Bandyopadhyay D,  Jacome-Pumar A. (2016). Comparing conditional survival functions with missing population marks in a competing risk model, Computational Statistics and Data Analysis, 95, 150-160

• C code to implement the Potts model in Jin I-H, Yuan Y and Bandyopadhyay D. (2016). A Bayesian hierarchical spatial model for dental caries assessment using non-Gaussian Markov random fields, The Annals of Applied Statistics, 10(2), 884-905

• WinBUGS code from Bandyopadhyay D, *Galvis DM and Lachos VH (2017). Augmented mixed models for clustered proportion data, Statistical Methods in Medical Research, 26(2), 880-897

• SAS macro from *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

• R package 'qrLMM' from *Galarza CE, Lachos VH and Bandyopadhyay D. (2017). Quantile regression for linear mixed models: A stochastic approximation EM approach, Statistics and Its Interface, 10(3), 471-482

• R code to generate p-values from the L1-distance tests (see Table 3) for assessing effects of the covariate BMI from Lan L, Bandyopadhyay D and Datta S. (2017). Nonparametric regression in clustered multistate current status data with informative cluster size, Statistica Neerlandica, 71(1), 31-57

• R code available in GitHub from *Chernokhouva A, Hussein A, Nkurunziza S and Bandyopadhyay D (2018). Bayesian inference in time-varying additive hazards model with applications to disease mapping, Environmetrics, 29(5-6), e2478 [Special Issue celebrating TIES 25th Anniversary]

• R code [with data]  from Zhao W, Lian H and Bandyopadhyay D. (2018). A partially linear additive model for clustered proportion data, Statistics in Medicine, 37(6), 1009-1030

• R code from Wu X, Guan T, Liu DJ, Leon-Novelo LG and Bandyopadhyay D. (2018). Adaptive-weight burden test for associations between quantitative traits and genotype data with complex correlations, The Annals of Applied Statistics,12(3), 1558-1582

• R package 'CensSpatial' from *Ordonez JA, Bandyopadhyay D, Lachos VH and Cabral CR. (2018). Geostatistical estimation and prediction for censored responses, Spatial Statistics, 23, 109-123

• R/JAGS codes available in GitHub from *Bhingare A, Sinha D, Pati D, Bandyopadhyay D and Lipsitz SR. (2019). Semiparametric Bayesian latent variable regression for skewed multivariate data, Biometrics, 75(2), 528-538

• R code available in GitHub from Zhang L and Bandyopadhyay D. (2020). A graphical model for skewed matrix-variate non-randomly missing data, Biostatistics, 21(2), e80-e97

• R code available in GitHub from *Guan Q, Reich BJ, Laber E and Bandyopadhyay D. (2020). Bayesian nonparametric policy search with applications to periodontal recall intervals, Journal of the American Statistical Association - Applications & Case Studies, 105(531), 1066-1078

• R code available in GitHub from #Xu J, Bandyopadhyay D, Chakraborty B, Mirzaei S and Michalowicz B. (2020). SMARTp: A SMART design for non-surgical treatment of chronic periodontitis with spatially-referenced and non-randomly missing skewed outcomes, Biometrical Journal, 62(2), 282-310;  RShiny link here; R package SMARTp here

• R code for simulation studies from *Jhuang A-T, Fuentes M, Bandyopadhyay D and Reich BJ. (2020).  Spatiotemporal signal detection using continuous shrinkage priors,  Statistics in Medicine, 39(13), 1817-1832 

• R package BAREB from *Li Y, Bandyopadhyay D, Xie F, Xu Y  (2020). BAREB: A Bayesian repulsive biclustering model for periodontal data,  Statistics in Medicine, 39(16), 2139-2151

• R package RASCO to alleviate spatial confounding in disease mapping (GLMMs) & survival analysis; led by PhD co-advisee Douglas Mesquita:
    (i)  *Azevedo DRM, Bandyopadhyay D, Prates MO, Abdel-Salam A-SG and Garcia D. (2020).  Assessing spatial confounding in cancer disease mapping using R, Cancer Reports, 3(4), e1263; 

 (ii) *Azevedo DRM, Prates MO and Bandyopadhyay D. (2021). MSPOCK: Alleviating spatial confounding in multivariate disease mapping models, Journal of Agricultural, Biological & Environmental Statistics, 26, 464-491. &  

(iii) *Azevedo DRM, Prates MO and Bandyopadhyay D. (2023). Alleviating spatial confounding in frailty models, Biostatistics, 24(4), 945-961

• R/C code from Shin Y, Sun S and Bandyopadhyay D. (2020). Impact of adoloscent obesity on middle-age health of women given data MAR, Biometrical Journal, 62(7), 1702-1716

• R package ICScure  from Lam KF, *Lee CY, Wong KY and Bandyopadhyay D. (2021). Marginal analysis of current status data with informative cluster size using a class of semiparametric transformation cure models, Statistics in Medicine, 40(10), 2400-2412

• R package clordr from *Wang P, Ma TF, Bandyopadhyay D, Tang Y and Zhu J. (2021). Composite likelihood inference for ordinal periodontal data with replicated spatial patterns, Statistics in Medicine,  40(26), 5871-5893

• R/C++ code available in GitHub from *Lan Z, Reich BJ and Bandyopadhyay D. (2021). A Bayesian semiparametric mixture modeling of spatially-dependent positive-definite matrices, with applications to diffusion tensor imaging, Canadian Journal of Statistics, 49(1), 129-149

• R code available in GitHub from Lock E, Bandyopadhyay D. (2021). Bayesian nonparametric multiway regression for clustered binomial data, Stat, 10(1), e378; Figure

• GitHub implementation for Swihart BJ and Bandyopadhyay D. (2021). Bridged parametric survival models: General paradigm and speed improvements, Computer Methods and Programs in Biomedicine, 206, 106115

• GitHub implementation for *Lan Z, Reich BJ and Bandyopadhyay D. (2021). Probabilistic diffusion MRI fiber-tracking using a directed acyclic graph auto-regressive model of positive definite matrices, Journal of Statistical Research, 55(1), 147-158

• GitHub implementation for Choi S, *Choi T, Cho H and Bandyopadhyay D. (2022). Weighted least-squares regression with competing risks data, Statistics in Medicine, 41(2), 227-241

• R code implementing Ke C, Bandyopadhyay D, Acunzo M and Winn R. (2022). Gene-screening in ultra-high dimensional right-censored lung cancer data, Onco, 2(4), 305-318

• R/C++ codes available in GitHub from *Lan Z, Reich BJ, Guinness J, Bandyopadhyay D, Ma L and Moeller G. (2022). Geostatistical modeling of positive-definite matrices: An application to diffusion tensor imaging, Biometrics, 78(2), 548-559

• R package drRML available in GitHub from Choi S, *Choi T, Lee H-Y, *Han SW and Bandyopadhyay D. (2022). Doubly-robust inference for differences in restricted mean lifetimes using pseudo-observations, Pharmaceutical Statistics, 21(6), 1185-1198

• R package MMIntAdd available in GitHub from Wang T, Bandyopadhyay D and Sinha S. (2022). Efficient estimation of the additive risks model for interval-censored data, In: Sun, J., Chen, DG. (eds) Emerging Topics in Modeling Interval-Censored Survival Data, ICSA Book Series in Statistics. Springer, Cham, Switzerland., 167-192 

• R package MMGOR from *Wang T, He K, Wei M, Bandyopadhyay D and Sinha S. (2023). Minorize-maximize algorithm for the generalized odds rate model for clustered current status data, The Canadian Journal of Statistics, 51(4), 1150-1170

• R packages skewBART available in GitHub from *Um S, Linero AR, Sinha D and Bandyopadhyay D. (2023). Bayesian Additive Regression Trees for Multivariate Skewed Responses, Statistics in Medicine, 42(3), 246-263

• R package BSTN available in GitHub from *Lee I, Mai Q, Sinha D, Zhang X and Bandyopadhyay D (2023). Bayesian regression analysis of skewed tensor responses, Biometrics, 79(3), 1814-1825 

• R implementation JointCSsurv available in GitHub from *Lee CY, Wong KY, Lam KF and Bandyopadhyay D (2023). A semiparametric joint model for cluster size and subunit-specific interval-censored outcomes, Biometrics, 32(8), 1494-1510 

• R package mspack2 available in GitHub from *Anyaso-Samuel S, Bandyopadhyay D and Datta S. (2023). Pseudo-value regression of clustered current status data with informative cluster size, Statistical Methods in Medical Research, 32(8), 1494-1510 

• GitHub implementation for Lu X, *Wang Y, Bandyopadhyay D and Bakoyannis G. (2023).  Sieve estimation of a class of partially linear transformation models with interval-censored competing risks data,  Statistica Sinica, 33, 685-704 

• R markdown demo for Ke C, Bandyopadhyay D and Sarkar D (2023). Gene screening for prognosis of non-muscle-invasive bladder carcinoma under competing risks endpoints, Cancers,15(2), 379

• GitHub (RStan) implementation from *Acharya S, Pati D, Bandyopadhyay D and Sun S. (2024). A monotone single-index model for missing-at-random longitudinal proportion data, Journal of Applied Statistics, 51(6), 1023-1040 

• R package ipcwqrPIC available in GitHub for implementation in **Kim Y, **Choi T, Park S, Choi S and Bandyopadhyay D. (2024). Inverse weighted quantile regression with partially interval-censored data, Biometrical Journal, 66(8), e70001

• R-Stan code for implementing the methodology in Datta J and Bandyopadhyay D. (2024). Bayesian variable shrinkage and selection in compositional data regression: Application to oral microbiome, Journal of the Indian Society for Probability and Statistics, 25, 491-515 

• R code for implementing the methodology in *Bedia EC, Cancho V, and Bandyopadhyay D (2024). A frailty model for multistate semi-competing risk data with applications to colon cancer, Journal of the Indian Society for Probability and Statistics, 25, 395-416 

• INLA implementation of the methodology in Sahoo I, Zhao J, Deng X, Gordon M, Cockburn M, Tossas K, Winn R and Bandyopadhyay D (2024). Lung cancer prevalence in Virginia: A spatial zipcode-level analysis via INLA, Current Oncology, 31, 1129-1144

• R/JAGS implementation in GitHub from Tong X, *Kim S, Bandyopadhyay D and Sun S. (2024). Association between body fat and body mass index from incomplete longitudinal proportion data: Findings from the Fels study, Journal of Data Science, 22(1), 116-137 

• R package PLScure for implementing the method proposed in #Lee C-Y, Wong KY and Bandyopadhyay D (2024). Partly linear single-index cure models with a nonparametric incidence link function, Statistical Methods in Medical Research, 33(3), 498-514

• R code for running simulation design from *Zhang CE, Al-Mosawi RR,  Bandyopadhyay D, Huang H and Lu X (2024+). Sieve estimation of the additive hazards model for bivariate current status data, (Accepted), Statistics in Biosciences

• R/C++/Matlab code implementating 2-stage global Fréchet regression from *Yan L, Zhang X, Lan Z, Bandyopadhyay D and Wu Y. (2024+). Variable screening and spatial smoothing in Fréchet regression with application to diffusion tensor imaging, (Accepted), The Annals of Applied Statistics 

• R package rankIC available in GitHub from *Choi T, Choi S and Bandyopadhyay D. (2024+). Rank estimation for the accelerated failure time model with partially interval-censored data, (Revision submitted), Statistica Sinica

• R package DSFDRC available in GitHub for implementing the method proposed in *Urmi AF, Ke C and Bandyopadhyay D. (2024+). α-KIDS: A novel feature evaluation in the ultrahigh-dimensional right-censored setting, with application to Head and Neck Cancer, (Revision submitted), Statistics in Medicine

• R code to implement the methodology proposed in *Han J., Ha I-D, Lee Y and Bandyopadhyay D. (2024+). A h-likelihood approach to fitting accelerated failure time models for clustered heavily censored data, (Revision submitted), Journal of Applied Statistics   

• R/C++ code to implement the methodology proposed in *Das S, Chae M, Pati D and Bandyopadhyay D. (2024+). A monotone single-index model for spatially-referenced multistate current status data, (Under Revision), Biometrics

• R package CensSpBayes implementing the method proposed in Sahoo I, Majumder S, Hazra A, Rappold AG and Bandyopadhyay D. (2024+). Computationally scalable Bayesian SPDE modeling for censored spatial responses, (Revision submitted), The New England Journal of Statistics in Data Science

• R package STMATREG implementing the methodology proposed in Liu Q, Srivastava S and Bandyopadhyay D. (2024+). Asynchronous distributed ECME algorithm for matrix-variate non-Gaussian responses, (Under Review), Statistics and Computing

• R package fastCRRC implementing the methodology proposed in *Defor E, Chiou SH, Srivastava S and Bandyopadhyay D. (2024+). Scalable Algorithms for Marginal Regression Analyses of Clustered Competing Risks Data, (Under Review), Stat

• R package MSIMST to implement the methodology proposed in Liu Q, Pati D and Bandyopadhyay D. (2024+). An interpretable single-index mixed-effects model for Non-Gaussian national survey data, (To be submitted)

• R package BSTT available in GitHub from *Lee I, Sinha D, Mai Q and Bandyopadhyay D. (2024+). A new class of skewed tensor distributions, (To be submitted)