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 235 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 10 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 3,312 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. 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.