orthoDr

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'.

Last 30 days

This package has been downloaded 217 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 3,298 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 55 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.