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orthoDr

The goal of orthoDr is to use an orthogonality constrained optimization algorithm to solve a variety of dimension reduction problems in the semiparametric framework.

Installation

You can install the released version of orthoDr from CRAN with:

  install.packages("orthoDr")

Implemented Methods

This package implements the orthogonality constrained (Stiefel manifold) optimization approach proposed by Wen & Yin (2013). A drop-in solver ortho_optim() works just the same as the optim() function. Relying on this optimization approach, we also implemented a collection of dimension reduction models for survival analysis, regression, and personalized medicine.

• CP-SIR, Forward, IR-CP and IR-Semi methods in Sun, Zhu, Wang & Zeng (2019)
• semi-SIR and semi-PHD in Ma & Zhu (2012)
• Direct and pseudo-Direct methods in Zhou, Zhu & Zeng (2021)

We also implemented several methods and functions for comparison, testing and utilization purposes

• hMave: This is a direct R translation of the hMave MATLAB code by Xia, Zhang & Xu (2010)
• pSAVE: partial-SAVE in Feng, Wen, Yu & Zhu (2013)
• dist_cross(): kernel distances matrix between two sets of data, as an extension of dist()
• distance(): distance correlation between two linear spaces
• silverman(): Silverman’s rule of thumb bandwidth

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.