alex hayes

@alexpghayes.com

postdoc @ stanford econ + incoming assistant prof @ oregon state statistics. networks, causal inference, contagion, measurement error, #rstats. he/him https://www.alexpghayes.com

Some LaTeX resources I'd appreciate: - A modern guide to best LaTeX practices - A concise guide to mathematical writing - Typst <-> LaTeX conversion gotchas - Claude skills for all of the above

@grimalkina.bsky.social i love your learning-opportunities claude skill -- do you know of anything similar for web chat interfaces? the web interface is much nicer than clis when discussing math since it renders latex i'm curious about user prompts both for myself and also to suggest to students!

what's the recommended workflow for checking the accessibility of a #quarto website? currently i'm setting format: html: axe: output: document in _quarto.yml and checking every page visually and it's not particularly efficient #rstats

Claude skills for reviewing manuscripts that I would appreciate: - stylistic advice for math writing (punctuating eqs, etc) - academic tropes (gaps, see quote) and linguistic oddities (gerund form) to avoid - structure of a good abstract/introduction - etc

TThomas Dietterich@tdietterich.bsky.social · 5mo ago

I encourage everyone to delete the phrase "To address this gap" in their papers. Change "To address this gap, we introduce..." with just "We introduce". All of this "gap" terminology is such a cliche. (looking at a lot of arXiv papers today)

Grant Witness is hiring! We're seeking a full-time data scientist to join our team building data resources to support journalism, litigation, and activism protecting science, public health, and the rule of law. grant-witness.us/apply.html #rstats

Job Opening: Data Scientist – Grant Witness

Grant Witness seeks a data scientist for a full-time position on our team tracking changes to U.S. federal grantmaking. We are also hiring for part-time/contract positions. Join us!

grant-witness.us

New paper! The Linear Representation Hypothesis is a powerful intuition for how language models work, but lacks formalization. We give a mathematical framework in which we can ask and answer a basic question: how many features can be stored under the hypothesis? 🧵 arxiv.org/abs/2602.11246

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A new release of the mgcv #RStats 📦 is out on CRAN and Simon Wood (U Edinburgh) has added some significant new features despite the small bump in version number: 🌟 scasm() for estimating GAMs with shape constrained smooths. Can be used with any family & smoothness selection is via the EFS method

Model is:

b3 <- scasm(
  y ~ s(x0, bs = "bs", k= k) + s(x1, bs = "sc", xt = "m+", k = k) +
         s(x2, bs = "bs", k = k) + s(x3, bs = "bs", k = k),
  family=poisson, bs=200
)

The second smooth `s(x1) is a shape constrained smooth with a positive monotonicity constraint (xt = "m+").

The `bs = 200` arguments uses 200 boostrap samples, which generates bootstrap distributions for each coefficient in the model. These bootstrap samples respect the shape constraints, while the usual +/- 2 SE credible intervals may not.

The uncertainty in the partial effects is shown by two credible interval bands; a dark blue central band is a 68% Bayesian credible interval, while the lighter blue outer interval is a 95% Bayesian credible interval.

The background of each panel is light grey with white grid lines, in a similar style to ggplot2's default theme.