High-dimensional data are ubiquitous in modern statistics. My work in this area focus on Bayesian methods for both variable selection and dimension reduction. In particular, we have developed an R package entitled BayesPen which performs both Bayesian variable selection and confounder selection. Attractive features of this approach are that it avoids MCMC in special cases, has excellent frequentist properties, and exploits interesting connections between Bayesian decision theory and penalized regression methods. For more information, please find the paper and the package.