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) <doi:10.48550/arXiv.2209.14502> "Fast Inference for Quantile Regression with Tens of Millions of Observations".

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: stats, Rcpp (≥ 1.0.5)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), lmtest (≥ 0.9), sandwich (≥ 3.0), microbenchmark (≥ 1.4), conquer (≥ 1.3.3)
Published: 2023-11-16
Author: Sokbae Lee [aut], Yuan Liao [aut], Myung Hwan Seo [aut], Youngki Shin [aut, cre]
Maintainer: Youngki Shin <shiny11 at>
License: GPL-3
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: SGDinference results


Reference manual: SGDinference.pdf
Vignettes: SGDinference: An R Vignette


Package source: SGDinference_0.1.0.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): SGDinference_0.1.0.tgz, r-release (arm64): SGDinference_0.1.0.tgz, r-oldrel (arm64): SGDinference_0.1.0.tgz, r-prerel (x86_64): SGDinference_0.1.0.tgz, r-release (x86_64): SGDinference_0.1.0.tgz


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