Inference with Stochastic Gradient Descent R package SGDinference version 0.1.0 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". Software 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) Youngki Shin <shiny11@mcmaster.ca> Comprehensive R Archive Network (CRAN) Sokbae Lee [aut], Yuan Liao [aut], Myung Hwan Seo [aut], Youngki Shin [aut, cre] GPL-3 2023-11-16 application/tgz https://CRAN.R-project.org/package=SGDinference doi:10.32614/CRAN.package.SGDinference