DESCRIPTION

Package: SGDinference
Type: Package
Title: Inference with Stochastic Gradient Descent
Version: 0.1.0
Authors@R: c(
    person("Sokbae", "Lee", email = "sl3841@columbia.edu", role = "aut"),
    person("Yuan", "Liao", email = "yuan.liao@rutgers.edu", role = "aut"),
    person("Myung Hwan", "Seo", email = "myunghseo@snu.ac.kr", role = "aut"),
    person("Youngki", "Shin", email = "shiny11@mcmaster.ca", role = c("aut", "cre")))
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) <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". 
License: GPL-3
Imports: 
    stats,
    Rcpp (>= 1.0.5)
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.2.3
Encoding: UTF-8
Suggests: 
    knitr,
    rmarkdown,
    testthat (>= 3.0.0),
    lmtest (>= 0.9),
    sandwich (>= 3.0),
    microbenchmark (>= 1.4),
    conquer (>= 1.3.3)
VignetteBuilder: knitr
Config/testthat/edition: 3
Depends: 
    R (>= 2.10)
LazyData: true
URL: https://github.com/SGDinference-Lab/SGDinference/
BugReports: https://github.com/SGDinference-Lab/SGDinference/issues