• 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 02/2024. It is on Github and CRAN. 

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

          References: 

• Fan, K., Jiang, Y., Ma, S., Wang, W. and Wu, C.*  (2024+). Robust Sparse Bayesian Regression for Longitudinal Gene-Environment Interactions.  

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

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

The R package has been released on CRAN since 09/2023. It is also on Github.

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

          References: 

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

• 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 11/2023. 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.

• R package: Bayenet  (Bayesian quantile elastic net for genetic study )       

• Author: Lu, X. and Wu, C.

The R package has been released on 05/2023. It is on Github and CRAN. 

          References: 

•  Lu, X., Ren, J., Ma, S. and Wu, C.*  (2024+). Bayesian quantile 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. It is on Github and CRAN. 

The core module of the package is developed in C++. Extensions to survival 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 updated on 01/2023. 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 12K 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]

  1. • 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 released on 05/2020. It is now available at both Github and CRAN.

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

         References: 

•  Ren, J., Zhou, F., Li, X., Ma, S., Jiang, Y. and Wu, C.*  (2022). 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.The core module of the package is developed in C++.

This package has been downloaded over 13K 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 09/2021.

This package has been downloaded over 25K 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 08/2022.

This package has been downloaded over 26K 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 50K times.