orthoDr: Semiparametric dimension reduction via orthogonality constrained optimization

Ruoqing Zhu, Jiyang Zhang, Ruilin Zhao, Peng Xu, Wenzhuo Zhou, Xin Zhang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)24-37
Number of pages14
JournalR Journal
Volume11
Issue number2
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'orthoDr: Semiparametric dimension reduction via orthogonality constrained optimization'. Together they form a unique fingerprint.

Cite this