Package hdlm: Regression Tables for High Dimensional Linear Model Estimation

We present the R package hdlm, created to facilitate the study of high dimensional datasets. Our emphasis is on the production of regression tables and a class ‘hdlm’ for which new extensions can be easily written. We model our work on the functionality given for linear and generalized linear models from the functions lm and glm in the recommended package stats. Reasonable default options have been selected so that the package may be used immediately by anyone familiar with the low dimensional variants; however, a generic procedure for using alternative point estimators is also provided. Two techniques are given for constructing high dimensional regression tables. The first uses the the two-stage approach of Wasserman and Roeder (2009), with the generalization proposed by Meinshausen, Meier, and Bühlmann (2009) to increase robustness, in order to calculate high-dimensional p values. We introduce and implement a novel method for generalizing these p value methods to confidence intervals. The second technique constructs regression tables using a hierarchical Bayesian approach solved via Gibbs Sampling MCMC. In this article, we focus on design choices made in the package, relevant computational issues, and approaches to changing the default options.