dbglm
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Fast generalised linear models by sampling and one-step polishing
This package fits generalised linear models to moderately large data sets stored in a relational database. The code has implementations for MonetDB and SQLite, but should be easy to adapt to any other database that has EXP and RAND.
The code takes a subsample of the data, fits the model in memory, then improves the estimate with one step of Fisher scoring computed with a single SQL aggregation query.