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SGDinference  

Inference with Stochastic Gradient Descent
View on CRAN: Click here


Download and install SGDinference package within the R console
Install from CRAN:
install.packages("SGDinference")

Install from Github:
library("remotes")
install_github("cran/SGDinference")

Install by package version:
library("remotes")
install_version("SGDinference", "0.1.0")



Attach the package and use:
library("SGDinference")
Maintained by
Youngki Shin
[Scholar Profile | Author Map]
First Published: 2023-11-16
Latest Update: 2023-11-16
Description:
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) "Fast and robust online inference with stochastic gradient descent via random scaling". (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) "Fast Inference for Quantile Regression with Tens of Millions of Observations".
How to cite:
Youngki Shin (2023). SGDinference: Inference with Stochastic Gradient Descent. R package version 0.1.0, https://cran.r-project.org/web/packages/SGDinference. Accessed 17 Feb. 2025.
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