orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained

Authors

Ruoqing Zhu, University of Illinois at Urbana-Champaign
Jiyang Zhang, University of Illinois at Urbana-Champaign
Ruilin Zhao, University of Pennsylvania
Peng Xu, Columbia University
Wenzhuo Zhou, University of Illinois at Urbana-Champaign
Xin Zhang, Florida State University

Date of this Version

12-2019

Document Type

Article

Citation

The R Journal (December 2019) 11(2); Editor: Michael J. Kane

Comments

Copyright 2019, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

orthoDr is a package in R that solves dimension reduction problems using orthogonality constrained optimization approach. The package serves as a unified framework for many regression and survival analysis dimension reduction models that utilize semiparametric estimating equations. The main computational machinery of orthoDr is a first-order algorithm developed by Wen and Yin (2012) for optimization within the Stiefel manifold. We implement the algorithm through Rcpp and OpenMP for fast computation. In addition, we developed a general-purpose solver for such constrained problems with user-specified objective functions, which works as a drop-in version of optim(). The package also serves as a platform for future methodology developments along this line of work.