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orthoDr: Semi-Parametric Dimension Reduction Models Using Orthogonality Constrained Optimization
Utilize an orthogonality constrained optimization algorithm of Wen & Yin (2013) <DOI:10.1007/s10107-012-0584-1> to solve a variety of dimension reduction problems in the semiparametric framework, such as Ma & Zhu (2012) <DOI:10.1080/01621459.2011.646925>, Ma & Zhu (2013) <DOI:10.1214/12-AOS1072>, Sun, Zhu, Wang & Zeng (2019) <DOI:10.1093/biomet/asy064> and Zhou, Zhu & Zeng (2021) <DOI:10.1093/biomet/asaa087>. The package also implements some existing dimension reduction methods such as hMave by Xia, Zhang, & Xu (2010) <DOI:10.1198/jasa.2009.tm09372> and partial SAVE by Feng, Wen & Zhu (2013) <DOI:10.1080/01621459.2012.746065>. It also serves as a general purpose optimization solver for problems with orthogonality constraints, i.e., in Stiefel manifold. Parallel computing for approximating the gradient is enabled through 'OpenMP'.
Getting started
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Package details |
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| Author | Ruilin Zhao [aut, cph], Ruoqing Zhu [aut, cre, cph] (<https://orcid.org/0000-0002-0753-5716>), Jiyang Zhang [aut, cph], Wenzhuo Zhou [aut, cph], Peng Xu [aut, cph], James Joseph Balamuta [ctb] (<https://orcid.org/0000-0003-2826-8458>) |
| Maintainer | Ruoqing Zhu <teazrq@gmail.com> |
| License | GPL (>= 2) |
| Version | 0.6.7 |
| URL | https://github.com/teazrq/orthoDr |
| Package repository | View on CRAN |
| Installation |
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