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

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

The R package has been released on CRAN since 09/2023 and last updated on 05/2025. It is also on Github.

The C++ based R package provides implementations of Bayesian regularized quantile regression incorporating exact sparsity under high-dimensional linear and quantile VC models. Valid robust Bayesian inferences under both models in the presence of heavy-tailed errors can be validated on finite samples. Additional models include robust Bayesian binary LASSO and robust Bayesian group LASSO. Codes for robust Bayesian regression with the horseshoe family of priors will be available soon.

          References: 

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

  • • 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  

    • 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. (In press). 

    • Fan, K., Subedi, S., Dissanayake, V.  and  Wu, C.*  (2025+). Robust Bayesian high-dimensional variable selection and inference with the horseshoe family of priors. [arXiv] 

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

• Author: Fan, K. and Wu, C.

The R package has been released on 04/2023 and updated on 09/2024 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++.      

          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. (In press).

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

• Author: Fan, K. and Wu, C.

The C++ based R package has been released in 11/2024 on both CRAN and Github. 

This package can be adopted to perform posterior inference in terms of valid Bayesian credible intervals on high-dimensional nonparametric and parametric fixed effects simultaneously. 

          References: 

• Fan, K., Ren, J., Ma, S. and Wu, C.*  (2025+). 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 has been released on 06/2023 and updated on 09/2024. It is on Github and CRAN. 

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

          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 has been released on 05/2023 and updated on 04/2024. It is on Github and CRAN. 

          References: 

•  Lu, X., Ren, J., Ma, S. and Wu, C.*  (2025+). Robust Bayesian elastic net with spike-and-slab priors.

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

• Author: Lu, X. and Wu, C.

The R package has been released on 05/2023 and updated on 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.     

          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 has been last updated on 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 15K 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 has been recently updated on 03/2024. 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 11K 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 has been released on 05/2019 and updated on 03/2024.The core module of the package is developed in C++.

This package has been downloaded over 16K 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 released on 11/2018.  

The most recent version has been released on 01/2024.

This package has been downloaded over 30K 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 has been released on 02/2024.

This package has been downloaded over 29K 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 58K times.