CRAN/E | SGDinference

SGDinference

Inference with Stochastic Gradient Descent

Installation

About

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) "Fast Inference for Quantile Regression with Tens of Millions of Observations".

github.com/SGDinference-Lab/SGDinference/
Bug report File report

Key Metrics

Version 0.1.0
R ≥ 3.5.0
Published 2023-11-16 214 days ago
Needs compilation? yes
License GPL-3
CRAN checks SGDinference results

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Maintainer

Maintainer

Youngki Shin

Authors

Sokbae Lee

aut

Yuan Liao

aut

Myung Hwan Seo

aut

Youngki Shin

aut / cre

Material

README
NEWS
Reference manual
Package source

Vignettes

SGDinference: An R Vignette

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Depends

R ≥ 3.5.0

Imports

stats
Rcpp ≥ 1.0.5

Suggests

knitr
rmarkdown
testthat ≥ 3.0.0
lmtest ≥ 0.9
sandwich ≥ 3.0
microbenchmark ≥ 1.4
conquer ≥ 1.3.3

LinkingTo

Rcpp
RcppArmadillo