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 DOI: 10.32614/CRAN.package.SGDinference Author: Sokbae Lee [aut], Yuan Liao [aut], Myung Hwan Seo [aut], Youngki Shin [aut, cre] Maintainer: Youngki Shin <shiny11 at mcmaster.ca> BugReports: https://github.com/SGDinference-Lab/SGDinference/issues License: GPL-3 URL: https://github.com/SGDinference-Lab/SGDinference/ NeedsCompilation: yes Materials: README NEWS CRAN checks: SGDinference results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=SGDinference to link to this page.
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