Packaged: 2017-11-02 02:26:12 UTC; Ruoqing Zhu

Author: Ruilin Zhao, Jiyang Zhang and Ruoqing Zhu
Description: 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>, and Sun, Zhu, Wang & Zeng (2017) <arXiv:1704.05046>. It also serves as a general purpose optimization solver for problems with orthogonality constraints. Parallel computing is enabled through `OpenMP'.
Imports: Rcpp (>= 0.12.12), survival, dr, pracma, plot3D, rgl, MASS
Packaged: 2018-03-25 18:29:09 UTC; Ruoqing Zhu

Description: 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>, and Sun, Zhu, Wang & Zeng (2017) <arXiv:1704.05046>. It also serves as a general purpose optimization solver for problems with orthogonality constraints. Parallel computing is enabled through OpenMP.
Packaged: 2018-03-24 22:38:10 UTC; Ruoqing Zhu

Title: Semi-Parametric Dimension Reduction Models Using Orthogonality
Author: Ruilin Zhao, Ruoqing Zhu, Jiyang Zhang, Wenzhuo Zhou and Peng Xu
Description: 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 (2017) <arXiv:1704.05046> and Zhou & Zhu (2018+) <arXiv:1802.06156>. It also serves as a general purpose optimization solver for problems with orthogonality constraints. Parallel computing for approximating the gradient is enabled through `OpenMP'.

Description: Utilize an orthogonality constrained optimization algorithm of
dimension reduction problems in the semiparametric framework, such as
and Zhou & Zhu (2018+) <arXiv:1802.06156>. It also serves as a general
purpose optimization solver for problems with orthogonality constraints.
Parallel computing for approximating the gradient is enabled