Package: SGDinference 0.1.0

Youngki Shin

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".

Authors:Sokbae Lee [aut], Yuan Liao [aut], Myung Hwan Seo [aut], Youngki Shin [aut, cre]

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SGDinference.pdf |SGDinference.html
SGDinference/json (API)
NEWS

# Install 'SGDinference' in R:
install.packages('SGDinference', repos = c('https://sgdinference-lab.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/sgdinference-lab/sgdinference/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

inferencesgdstochastic-gradient-descentsubgradient

3.70 score 1 stars 4 scripts 149 downloads 4 exports 2 dependencies

Last updated 1 years agofrom:a66700a9f7. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-win-x86_64OKNov 12 2024
R-4.5-linux-x86_64OKNov 12 2024
R-4.4-win-x86_64OKNov 12 2024
R-4.4-mac-x86_64OKNov 12 2024
R-4.4-mac-aarch64OKNov 12 2024
R-4.3-win-x86_64OKNov 12 2024
R-4.3-mac-x86_64OKNov 12 2024
R-4.3-mac-aarch64OKNov 12 2024

Exports:sgd_lmsgd_qrsgdi_lmsgdi_qr

Dependencies:RcppRcppArmadillo

SGDinference: An R Vignette

Rendered fromSGDinference.Rmdusingknitr::rmarkdownon Nov 12 2024.

Last update: 2023-11-15
Started: 2022-09-17