The R Journal: article published in 2019, volume 11:2

orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization
Ruoqing Zhu, Jiyang Zhang, Ruilin Zhao, Peng Xu, Wenzhuo Zhou and Xin Zhang , The R Journal (2019) 11:2, pages 24-37.

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.

Received: ; online 2019-07-30, supplementary material, (14.2 Kb)
CRAN packages: orthoDr, Rcpp, RcppArmadillo, ManifoldOpthm
CRAN Task Views implied by cited CRAN packages: NumericalMathematics, HighPerformanceComputing

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2019-006,
  author = {Ruoqing Zhu and Jiyang Zhang and Ruilin Zhao and Peng Xu and
          Wenzhuo Zhou and Xin Zhang},
  title = {{orthoDr: Semiparametric Dimension Reduction via
          Orthogonality Constrained Optimization}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-006},
  url = {https://doi.org/10.32614/RJ-2019-006},
  pages = {24--37},
  volume = {11},
  number = {2}
}