PyFixest Sprint We will be doing a 3-day PyFixest sprint in two weeks - are there any features / regression-based methods widely useful to applied researchers you'd like us to explore/ add?

@jacobtomlinson.dev has been working on sphinx llm - core idea: provide a .md file for each page of your docs. Does this really help LLMs navigate the documentation? And what about an llms.txt? If yes, what are best practices? github.com/NVIDIA/sphin...

Strong agree. And do read the paper! It illustrates very nicely how much thought and creativity has gone into designing fixest's API and performance optimizations which are really the two things that make fixest as great.

Things are grim. But in more frivolous news... @jamesbrandecon.bsky.social and I have been chipping away at `dbreg`, a 📦 for running big regression models on database backends. For the right kinds of problems, the speed-ups are near magical. Website: grantmcdermott.com/dbreg/ #rstats [1/2]

Very flattering (and exciting!) to see that Claude Code, even though prompted to use other packages, decided that it was a good choice to fit a TWFE regression in Python via PyFixest =)

There is a new-ish Python package, moderndid, that implements almost all of the "modern" DiD estimators: github.com/jordandekler...

TIL that `maketables` already supports regression tables via `typst`: py-econometrics.github.io/maketables/d... Happy holidays everyone! =)

Btw, I think there is a great IO / market structure research question there based on GitHub data - has the incumbency effect of established OSS projects increased due to greater prominence in LLM training data?

As everyone is querying LLMs for research these days, sometimes I check PyFixest's SEO game by asking "How can I estimate fixed effects regression problems in Python?". GTP: a) linearmodels b) statsmodels c) hand rolled d) call Julia / R from Python. Pf not (yet) recommended for arcane reasons.

github.com/grantmcdermo... 👀

Very quick support for CausalPy @benvincent.bsky.social. Draft PR will follow in a bit

`maketables` now has plug-in tooling - this means you can support maketables with your library without changing the maketables code base. This will hopefully be pretty useful for package developers, but also researchers developing their own estimators / in their own codebase.

Here is the recording of my talk PyData Berlin 2025: Introduction to Stochastic Variational Inference with NumPyro Notebook: juanitorduz.github.io/intro_svi/ youtu.be/wG0no-mUMf0?... #pydata #berlin #bayes

#rstats Please welcome Jarl, a new R linter. Jarl is a CLI tool with extensions in VS Code, Positron, and Zed. It can check thousands of lines of R code in milliseconds. Jarl provides several output formats, a Github Actions workflow, and more. Blog post: www.etiennebacher.com/posts/2025-1...

Quickly added support for @apoorvalal.com 's duckreg - took approximately 60 minutes! github.com/py-econometr...

Our py-econometrics org has a new package: `maketables` is now installable from PyPi! You can create regression tables for PyFixest, statsmodels, linearmodels, and save them as html, latex, or word. github: github.com/py-econometr... docs: py-econometrics.github.io/maketables/

Trying to learn more about fixed effects. I wrote this for me, maybe this is useful for you too dpananos.github.io/posts/2025-1...

I am very happy that 𝗣𝘆𝗙𝗶𝘅𝗲𝘀𝘁 𝟬.𝟰𝟬.𝟬/𝟬.𝟰𝟬.𝟭 is now available on PyPi! The release includes a few technical changes and exciting new features =)

Got a new PC and getting back to pyfixest dev is a breeze with pixi (via conda-forge, which seems to get too little love?). This just works: clone pf, install pixi, type pixi r tests in the shell, and then pixi installs Python, R, all Python and R deps, all R deps, and then starts running tests.

#rstats #econsky fixest v0.13.0 is finally out! It's still about making OLS and GLM estimations easy. Some major changes: - *default* VCOV becomes iid always! - singletons are removed by default! See all the changes here: github.com/lrberge/fixe...

#rstats #econsky PSA: The next release of `fixest` will include some important changes (plus cool new features). E.g. Fixed-effects regs will now default to 'iid' SEs rather than clustered. github.com/lrberge/fixe... You can install and test drive the dev version from R-universe; see the README.

Vis method for decomposition now merged to main, feedback welcome!

Love to see this, of course =)

Currently revamping the default output view for the Gelbach Decomposition. Goal is to help both academics but also data scientists in industry to communicate results easily. Are the column and panel headers immediately clear? Any better way to summarize decomp results?

Really awesome new formulaic feature! We'll update pyfixest to make it happen (and might get some perf improvements thanks to polars along the way).

𝐏𝐲𝐅𝐢𝐱𝐞𝐬𝐭 0.30.1 is on 𝐏𝐲𝐏𝐢 and I am extremely happy with this release, mostly because of two 𝐧𝐞𝐰 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬: a Rust back end for all performance critical parts and quantile regression.

nerdsniped by @s3alfisc.bsky.social's pyfixest quantile regression PR into implementing a bunch of QR solution methods. Anyone have strong views on these things [was taught the LP one and good alt solvers exist, FN - which is implemented in quantreg - is very fast] gist.github.com/apoorvalal/3...

Presenting an invitation to the open source python econometrics ecosystem with @s3alfisc.bsky.social on Thursday at a NABE webinar. Please come [and/or send us PRs!] They also have talks from @jondr44.bsky.social and @lihualei.bsky.social lined up for later in the summer nabe.com/NABE/Events/...

Great slides! ... and we have a Python implementation for you in pyfixest: py-econometrics.github.io/pyfixest/ref...

Hi, I am looking for someone with a Stata license to help me verify an implementation for cluster robust errors in quantile regression as implemented in the qreg2 routine. Any help would be much appreciated! Gh issue with details here: github.com/py-econometr...

Reading the Koenker book on quantile regression and having a blast. Top quotes: "It is a somewhat unhappy fact of life that the asymptotic precision of quantile estimates in general (...) depend on the reciprocal of a density function ..."

I've started to work on a quantile regression implementation for pyfixest. There's a basic PR that implements the Frisch-Newton solver in numpy + a quantreg interface. Still plenty of things to do (standard errors, performance improvements), so please let me know if you want to contribute!

Blog post (fixest vignette) on "Collinearity": something everybody faces one day but for which there's not much guidance. Trying to clarify how econometrics software handle collinearity and what one should do about it. #RStats #EconSky Comments welcome lrberge.github.io/fixest/artic...

PyFixest 0.29 is on PyPi! - We have updated how small sample adjustments are computed. PyFixest inference matches fixest now 100%, out of the box and all of the time! - We have added support for fully saturated event studies, which in turn enables @apoorvalal.com's DiD specification tests.

I am currently revamping small sample defaults of pyfixest (the goal is to match fixest SEs out of the box). Now some unit tests for Poisson comparing Python vs R fail for derived statistics (tstats, pvalues, CIs) because (I think) of restrictive tolerances.

New paper, of the "your toys are probably fine, just make sure they don't have any serrated edges" variety

TIL I learned that of course you can set breakpoints in pytest scripts, you just have to move up the callstack by typing "u"... Increasingly coming to the conclusion that whenever there is a Python dev tool that I think everyone would want to have, it's already been shipped with the main libraries.

Thanks to @apoorvalal.com, we now have some benchmarks for PyFixest on the GPU: 2-3x improvements for large problems via JAX relative to numba! We also released pyfixest 0.28.0, mostly with small bug fixes, but it also contains JAX support and GLMs (no fixed effects yet) as experimental features.

More cool things coming to pyfixest!

NEW R PACKAGE: I created the {fetwfe} package to implement fused extended two-way fixed effects (FETWFE), my estimator for difference-in-difference under staggered adoptions. You can install and use it by following the instructions on GitHub here: github.com/gregfaletto/.... Short thread:

I’d like to set up some benchmarks for pyfixest using a cloud service and would like access to multiple threads and a GPU. Any recommendations? Colab pro only seems to offer two threads. Sane pricing and ease of use are the main criteria. Maybe @apoorvalal.com @gmcd.bsky.social @alemartinello.com ?

Something cool for the next pyfixest release 😊

I just released Coding for Economists v1.0.2. This update fixes a lot of minor issues and switches to Astral's uv as the recommended package manager. We have some new contributors too. And, big news, CfE also hit 750 stars! ⭐⭐⭐ aeturrell.github.io/coding-for-e...

`etwfe` v0.5.0 is now available on CRAN (and R-universe). grantmcdermott.com/etwfe/ As a reminder, `etwfe` makes it easy to estimate Extended TwoWay Fixed Effects a la @jmwooldridge.bsky.social. Super flexible and good for nonlinear models, recovering heterogeneous TEs, etc. #econsky #rstats 1/3

Wonderful! Cheers Dear readers, fyi we are doing a #Python update for #GaborsDataAnalysis github.com/gabors-data-...

𝗣𝘆𝗙𝗶𝘅𝗲𝘀𝘁 𝟬.𝟮𝟳: (𝗣𝗿𝗲-𝗖𝗵𝗿𝗶𝘀𝘁𝗺𝗮𝘀 🎅) 𝗥𝗲𝗹𝗲𝗮𝘀𝗲 Hi all, a new version of PyFixest (0.27) has found it's way to PyPi! Highlights: we now support @gelbach.bsky.social’s regression decomposition & Westfall and Young's multiple testing correction.

We have a new notebook that motivates multiple testing corrections and shows how to use them via pyfixest. At the end, it produces this pretty figure: py-econometrics.github.io/pyfixest/mul...

Call for Help: As AE for JSS, I am looking for reviewers for a neat paper (and #Rstats package) with low/mid/high precision float support, something of interest for (very) large models and machine learning. If you,dear reader, could review this please get in touch via email. Thanks in advance.

What's the standard approach for synthetic control with many treated units? One at the time? If the goal is to match the average outcome in the treatment group over time, why not estimate the propensity score using daily outcomes as covariates, and then build a SC using propensity weights?

As it’s still a little under the radar - @vincentab.bsky.social & team have also developed a Python version of marginaleffects: github.com/vincentarelb... Works with statsmodels and pyfixest!