OSIRCR
Cosine Regression-Based Online Sliced Inverse Regression Algorithm
In high-dimensional streaming data analysis, extracting core periodic features under real-time constraints remains challenging. Traditional dimension reduction methods fail to adapt to incremental data and yield low accuracy due to irrelevant variables. This package provides the Online Sliced Inverse Regression framework for cosine regression with high-dimensional irrelevant variables. It integrates subspace extraction of sliced inverse regression and incremental learning of online algorithms to efficiently handle periodic streaming data. Cai, Z., Li, R., & Zhu, L. (2020) <doi:10.48550/arXiv.2002.02795>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.9 |
rolling linux/jammy R-4.5 | OSIRCR_0.2.9.tar.gz |
20.4 KiB |
0.2.9 |
rolling linux/noble R-4.5 | OSIRCR_0.2.9.tar.gz |
20.3 KiB |
0.2.9 |
rolling source/ R- | OSIRCR_0.2.9.tar.gz |
6.6 KiB |
0.2.9 |
latest linux/jammy R-4.5 | OSIRCR_0.2.9.tar.gz |
20.4 KiB |
0.2.9 |
latest linux/noble R-4.5 | OSIRCR_0.2.9.tar.gz |
20.3 KiB |
0.2.9 |
latest source/ R- | OSIRCR_0.2.9.tar.gz |
6.6 KiB |
0.2.9 |
2026-04-23 source/ R- | OSIRCR_0.2.9.tar.gz |
0 B |