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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# sgmm
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<!-- Gentle introduction TO BE ADDED
__SGDinference__ is an R package that provides 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:
- 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).
<https://doi.org/10.1609/aaai.v36i7.20701>.
- 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] <https://doi.org/10.48550/arXiv.2209.14502>.
-->
## Installation
You can install the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools") # if you have not installed "devtools" package
devtools::install_github("SGDinference-Lab/sgmm")
```
We begin by calling the **sgmm** package.
```{r setup}
library(sgmm)
```