SGDinference
Inference with Stochastic Gradient Descent
Estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. The inference procedure handles cross-sectional data sequentially: (i) updating the parameter estimate with each incoming "new observation", (ii) aggregating it as a Polyak-Ruppert average, and (iii) computing an asymptotically pivotal statistic for inference through random scaling. The methodology used in the 'SGDinference' package is described in detail in the following papers: (i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2022) <doi:10.1609/aaai.v36i7.20701> "Fast and robust online inference with stochastic gradient descent via random scaling". (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) <arXiv:2209.14502> "Fast Inference for Quantile Regression with Tens of Millions of Observations".
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | SGDinference_0.1.0.tar.gz |
417.5 KiB |
0.1.0 |
rolling linux/noble R-4.5 | SGDinference_0.1.0.tar.gz |
422.6 KiB |
0.1.0 |
rolling source/ R- | SGDinference_0.1.0.tar.gz |
321.3 KiB |
0.1.0 |
latest linux/jammy R-4.5 | SGDinference_0.1.0.tar.gz |
417.5 KiB |
0.1.0 |
latest linux/noble R-4.5 | SGDinference_0.1.0.tar.gz |
422.6 KiB |
0.1.0 |
latest source/ R- | SGDinference_0.1.0.tar.gz |
321.3 KiB |
0.1.0 |
2026-04-26 source/ R- | SGDinference_0.1.0.tar.gz |
321.3 KiB |
0.1.0 |
2026-04-23 source/ R- | SGDinference_0.1.0.tar.gz |
321.3 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | SGDinference_0.1.0.zip |
747.7 KiB |
0.1.0 |
2025-04-20 source/ R- | SGDinference_0.1.0.tar.gz |
321.3 KiB |