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A large body of research has focused on theory and computation for variableselection techniques for high dimensional data. There has been substantiallyless work in the big tall data paradigm, where the number of variables may belarge, but the number of observations is much larger. The orthogonalizingexpectation maximization (OEM) algorithm is one approach for computation ofpenalized models which excels in the big tall data regime. The oem package isan efficient implementation of the OEM algorithm which provides a multitude ofcomputation routines with a focus on big tall data, such as a function forout-of-memory computation, for large-scale parallel computation of penalizedregression models. Furthermore, in this paper we propose a specializedimplementation of the OEM algorithm for cross validation, dramatically reducingthe computing time for cross validation over a naive implementation.

Fast Penalized Regression and Cross Validation for Tall Data with the oem Package