py-econometrics/pyfixest

AI Summary: Update the PyFixest package to utilize Formulaic's native narwhal support for improved performance and potentially offer native Polars DataFrame support for input and output. This involves modifying the model_matrix function to use narwhals syntax, removing pandas conversion steps, and updating the Formulaic dependency.

AI Summary: Implement a sparse Frisch-Newton algorithm for quantile regression in the PyFixest Python package. This involves translating the existing dense matrix implementation to use sparse SciPy matrices, adding support for categorical variables, and making it selectable via a new `method` argument ('sfn' and 'psfn') in the `pf.quantreg()` function.

AI Summary: Update the PyFixest documentation's "Getting Started" vignette to explicitly show how to access key attributes (like adjusted R-squared) from the `feols` summary output. This involves identifying the relevant attributes within the `feols` object and adding clear examples to the vignette.

AI Summary: The task involves translating the R code examples from Peng Ding's open-access book "Linear Models" into Python using the `pyfixest` package and potentially other related libraries. This would create either documentation examples within `pyfixest` or a separate applications repository, similar to `mlpack`'s examples, to showcase the package's capabilities and provide learning resources.