• R package: pqrBayes  (The Bayesian Penalized Quantile Regression) 

• Author: Fan, K., Wu, C., Ren, J., Li, X. and Zhou, F..

The R package was initially released on CRAN in 09/2023 and most recently updated in 04/2026. It is also on Github.

The C++ based R package provides implementations of Bayesian regularized quantile regression with two major classes of shrinkage priors: the spike-and-slab priors and the horseshoe family of priors. Valid robust Bayesian inference under sparse models in the presence of heavy-tailed errors can be validated on finite samples. 

This package has been downloaded over 11K times.  

          References: 

• • • Fan, K., Subedi, S., Yang, G., Lu, X., Ren, J.* and Wu, C.*  (2024). Is Seeing Believing? A Practitioner's Perspective on High-dimensional Statistical Inference in Cancer Genomics Studies. Entropy. 26(9). 794  

    • Zhou, F., Ren, J., Ma, S. and Wu, C.*  (2023). The Bayesian regularized quantile varying coefficient model. Computational Statistics & Data Analysis. 187, 107808. 

    • Ren, J., Zhou, F., Li, X., Ma, S., Jiang, Y. and Wu, C.*  (2023). Robust Bayesian variable selection for gene-environment interactions. Biometrics. 79(2), 684-694.

    • Fan, K. and Wu, C.*  (2025). A New Robust Binary Bayesian LASSO. Stat. 14 (3), e70078

    • Fan, K., Subedi, S., Dissanayake, V.  and  Wu, C.*  (2026). Robust Bayesian high-dimensional variable selection and inference with the horseshoe family of priors. Computational Statistics & Data Analysis. 219, 108358 

• R package: mixedBayes  (The Bayesian longitudinal regularized quantile mixed model)       

• Author: Fan, K., Shejuty, D. and Wu, C.

The R package was initially released on 04/2023 and most recently updated in 08/2026 with a more efficient Gibbs sampler in the longitudinal setting. It is on both CRAN and Github. The core module of the package is developed in C++.  

This package has been downloaded over 12K times.  

          References: 

• Fan, K., Jiang, Y., Ma, S., Wang, W. and Wu, C.*  (2025). Robust Sparse Bayesian Regression for Longitudinal Gene-Environment Interactions. Journal of the Royal Statistical Society Series C: Applied Statistics. 74(5): 1372–1394

• Li, X., Fan, K., and Wu, C*. (2025). Prioritizing Longitudinal Gene–Environment Interactions Using an FDR-Assisted Robust Bayesian Linear Mixed Model. Algorithms, 18(1), 728. [Link] 

• Fan, K.., Li, X., Devnath, S., Olson, B. and Wu, C.*  (2026). MixedBayes: An R Package for Longitudinal Gene–Environment Interaction Analysis Using Robust Sparse Bayesian Mixed Models. Entropy. 28(6), 649. [Link][R examples]

• R package: Blend  (Robust Bayesian longitudinal regularized semiparametric mixed model)       

• Author: Fan, K. and Wu, C.

The C++ based R package was initially released in 11/2024 and most recently updated in 09/2026 on both CRAN and Github. 

This package can be adopted to perform valid inference using Bayesian credible intervals with frequentist nominal coverage on high-dimensional nonparametric and parametric fixed effects simultaneously. 

This package has been downloaded over 5K times.  

          References: 

• Fan, K., Ren, J., Ma, S. and Wu, C.*  (2026+). Robust Bayesian variable selection and inference under misspecified nonparametric mixed models in longitudinal studies. [Blend] 

• R package: emBayes  (Robust Bayesian variable selection via Expectation-Maximization)       

• Author: Liu, Y. and Wu, C.

The R package was initially released in 06/2023 and most recently updated in 09/2024. It is on Github and CRAN. 

The core module of the package is developed in C++.      

                This package has been downloaded over 9K times.  

          References: 

•  Liu, Y., Ren, J., Ma, S. and Wu, C.*  (2024). The Spike-and-Slab Quantile LASSO for robust variable selection in cancer genomics studies. Statistics in Medicine. 43(26): 4928-4983   [arXiv] 

• R package: Bayenet  (Robust Bayesian elastic net)       

• Author: Lu, X. and Wu, C.

The R package was initially released in 05/2023 and most recently updated in 04/2024. It is on Github and CRAN. 

                This package has been downloaded over 10K times.  

          References: 

• Lu, X.., Ren, J., Ma, S. and Wu, C.*  (2026). Variable selection and inference with a new robust Bayesian elastic net. Journal of Statistical Computation and Simulation. 1-25. (In press) [Bayenet]

• R package: marble  (Robust marginal Bayesian variable selection for Gene-environment interactions)       

• Author: Lu, X. and Wu, C.

The R package was initially released in 05/2023 and most recently updated in 04/2024. It is on Github and CRAN. 

The core module of the package is developed in C++. Extensions to survival and longitudinal responses will be included in the follow-up update the package.     

                This package has been downloaded over 10K times.  

          References: 

• Lu, X., Fan, K., Ren, J., and Wu, C.*  (2021). Identifying Gene-environment interactions with robust marginal Bayesian variable selection. Frontiers in Genetics. 12:667074. [marble]

• R package: springer (Sparse group variable selection for Gene-environment interactions in the longitudinal study)       

• Author: Zhou, F., Liu, Y., Lu, X., Ren, J., and Wu, C.

The R package was most recently updated in 02/2024. It is now available at both Github and CRAN.

The core module of the package is developed in C++.      

                This package has been downloaded over 21K times.

          References: 

•  Zhou, F., Lu, X, Ren, J., Fan, K., Ma, S. and Wu, C.*  (2022). Sparse group variable selection for Gene-environment interactions in the longitudinal study. Genetic Epidemiology. 46(5-6), 317-340 . [arXiv]

  • • Zhou, F., Liu,Y., Ren, J., Wang, W. and Wu, C.*  (2023). Springer: an R package for bi-level variable selection of high-dimensional longitudinal data. Frontiers in Genetics.14:1088223. 

• R package: roben (Robust Bayesian variable selection for gene-environment interactions)        

• Author: Ren, J., Zhou, F., Li, X., and Wu, C.

The R package was most recently updated in 02/2025. It is now available at both Github and CRAN.

The core module of the package is developed in C++.       

This package has been downloaded over 22K times.

         References: 

•  Ren, J., Zhou, F., Li, X., Ma, S., Jiang, Y. and Wu, C.*  (2023). Robust Bayesian variable selection for gene-environment interactions. Biometrics. 79(2), 684-694. [arXiv]

• R package: spinBayes (Semi-parametric Gene-Environment Interaction via Bayesian variable selection)

• • • Author: Ren, J., Zhou, F., Li, X., Wu, C. and Jiang, Y.

    • Reference Manual: link

The package was initially released in 05/2019 and most recently updated in 03/2024.The core module of the package is developed in C++.

This package has been downloaded over 20K times.

          References: 

• • •  Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang,Y. and Wu, C.*  (2020). Semi-parametric Bayesian variable selection for Gene-Environment interactions. Statistics in Medicine. 39(5): 617–638  [arXiv]. 

• R package: interep (Interaction Analysis of Repeated Measure Data) 

• • • Author: Zhou, F., Ren, J., Liu, Y.,  Li, X., Wu, C, Jiang, Y.

    • Reference Manual: link

The new version, with the core module developed in C++, has been updated in 11/2018.  

The most recent version was released in 01/2024.

This package has been downloaded over 36K times.

          References:

• • • Zhou, F., Ren,J., Li, G., Jiang, Y., Li, X., Wang, W. and Wu, C.* (2019). Penalized variable selection for Lipid-Environment interactions in a longitudinal lipidomics study. Genes. 10(12), 1002  

    • Zhou, F., Ren, J., Liu, Y., Li, X., Wang, W and Wu, C*. (2022). Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data. Genes. 13(3), 544.

• R package: regnet (Network-based Regularization for Generalized Linear Models) 

• • • Author: Ren, J., Luann, J., Du, Y., Wu, C., Jiang, Y., Liu, J.

    • Reference Manual: link

The core module (in C++) has been updated on 10/2017. The most recent version was released in 02/2024.

This package has been downloaded over 35K times.

          References:

• • • Ren, J., He, T., Li, Y., Liu, S., Du, Y.,  Jiang, Y. and Wu, C.* (2017). Network-based regularization for high dimensional SNP data in the case–control study of Type 2 diabetes. BMC Genetics. 18(1): 44

    • Ren, J., Du, Y., Li, S., Ma, S., Jiang,Y. and Wu, C.* (2019) Robust network-based regularization and variable selection for high dimensional genomics data in cancer prognosis. Genetic Epidemiology. 43(3): 276–291

• R package: accrual (Bayesian Accrual Prediction)

• • • Author: Liu,J., Jiang,Y., Wu, C., Simon, S., Mayo, M., Raghavan, R., Gajewski, B.

    • Reference Manual:  link

This package has been downloaded over 66K times.