link YOCO

readme.md

@@ -1,6 +1,6 @@
 # `duckreg` : very fast out-of-memory regressions with `duckdb`
 
-Package to run stratified out-of-memory regressions with duckdb. The package is a wrapper around the `duckdb` package and provides a simple interface to run regressions on very large datasets that do not fit in memory by reducing the data to a set of summary statistics and runs weighted least squares with frequency weights.
+python 0ackage to run stratified/saturated regressions out-of-memory with duckdb. The package is a wrapper around the `duckdb` package and provides a simple interface to run regressions on very large datasets that do not fit in memory by reducing the data to a set of summary statistics and runs weighted least squares with frequency weights. Robust standard errors are computed from sufficient statistics, while clustered standard errors are computed using the cluster bootstrap. 
 
 See examples in `notebooks/introduction.ipynb`.
 
@@ -27,7 +27,8 @@ references:
 
 methods:
 + [Arkhangelsky and Imbens (2023)](https://arxiv.org/abs/1807.02099)
-+ [Wooldridge 2021](https://www.researchgate.net/publication/353938385_Two-Way_Fixed_Effects_the_Two-Way_Mundlak_Regression_and_Difference-in-Differences_Estimators).
++ [Wooldridge 2021](https://www.researchgate.net/publication/353938385_Two-Way_Fixed_Effects_the_Two-Way_Mundlak_Regression_and_Difference-in-Differences_Estimators)
++ [Wong et al 2021](https://arxiv.org/abs/2102.11297)
 
 libraries:
 + [Grant McDermott's duckdb lecture](https://grantmcdermott.com/duckdb-polars/)