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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-006.zip
orthoDr, Rcpp, RcppArmadillo, ManifoldOpthm
NumericalMathematics, HighPerformanceComputing
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For attribution, please cite this work as
Zhu, et al., "The R Journal: orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization", The R Journal, 2019
BibTeX citation
@article{RJ-2019-006,
author = {Zhu, Ruoqing and Zhang, Jiyang and Zhao, Ruilin and Xu, Peng and Zhou, Wenzhuo and Zhang, Xin},
title = {The R Journal: orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-006},
doi = {10.32614/RJ-2019-006},
volume = {11},
issue = {2},
issn = {2073-4859},
pages = {24-37}
}