[caerbannogwhite]

Thank you, that's quite a lot of great quality feedback!

Agreed, it's not production-grade yet, and robust standard errors are the priority. HC and cluster-robust SEs are the biggest credibility gap, which is why I just added an (Eigen-based) linalg kernel for the next release: it's the groundwork so the regression fitter can ship HC0–HC3 and clustered SEs (and bootstrapped, via the existing bootstrap aggregate). Margins and GLMs (IRLS on the same kernel, so not really a backend rewrite) are the next layers.

About the scope, agreed, and already narrowing. I'll credit posit ggsql as the real grammar-of-graphics tool; mine stays a minimal built-in. The plan is to go deep on the statistical core and lean on the DuckDB ecosystem for the rest.

For the market fit I'd frame it a bit differently. Not trying to pry anyone off Stata/R for rigorous work, I know that those aren't going anywhere soon. The niche is stats where the data already lives: people in SQL/DuckDB who currently round-trip to R/Python for a regression. R can run in the browser now (webR), but not in the same engine as the data. The stats-duck runs inside duckdb-wasm, so there's no separate runtime and no marshalling. And DuckDB is already far faster than R for the ETL/wrangling around the stats.

The plan is to figure out from real usage. For now, I'll focus on the core.