From 6f82029b771ca9444bb9e522c67df0d2a6f5aa56 Mon Sep 17 00:00:00 2001 From: "Sokbae \"Simon\" Lee" Date: Tue, 7 Nov 2023 22:37:00 -0500 Subject: [PATCH] can-comments.md added --- .Rbuildignore | 1 + DESCRIPTION | 8 ++++---- cran-comments.md | 5 ++++- vignettes/SGDinference.Rmd | 2 +- 4 files changed, 10 insertions(+), 6 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index de075ed..0544f9b 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -3,3 +3,4 @@ ^LICENSE\.md$ ^\.github$ ^README\.Rmd$ +^cran-comments\.md$ diff --git a/DESCRIPTION b/DESCRIPTION index a118912..2e55790 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,20 +1,20 @@ Package: SGDinference Type: Package -Title: Inference with Stochastic (sub-)Gradient Descent +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: The package provides estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. +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. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 7, pp. 7381-7389). . - (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y., 2023. Fast Inference for Quantile Regression with Tens of Millions of Observations. arXiv:2209.14502 [econ.EM] . + (i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y., 2022. Fast and robust online inference with stochastic gradient descent via random scaling. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 7, pp. 7381-7389). . + (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y., 2023. Fast Inference for Quantile Regression with Tens of Millions of Observations. . . License: GPL-3 Imports: stats, diff --git a/cran-comments.md b/cran-comments.md index a5992a0..858617d 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,2 +1,5 @@ ## R CMD check results -* There were no ERRORs or WARNINGs. \ No newline at end of file + +0 errors | 0 warnings | 1 note + +* This is a new release. diff --git a/vignettes/SGDinference.Rmd b/vignettes/SGDinference.Rmd index c128747..a858fd3 100644 --- a/vignettes/SGDinference.Rmd +++ b/vignettes/SGDinference.Rmd @@ -1,6 +1,6 @@ --- title: "SGDinference: An R Vignette" -output: rmarkdown::pdf_document +output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{SGDinference: An R Vignette} %\VignetteEngine{knitr::rmarkdown}