formulaic
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matthewwardrop
A high-performance implementation of Wilkinson formulas for Python.
IS it possible to use a "fitted" transformer and evaluate a new (however, similar dataset)? Let's have the following example: ```python import pandas from formulaic import Formula df = pandas.DataFrame({...
This allows `Term` subclasses to label model matrix columns differently based on context; e.g. if a column is a random effect column, then it might be labelled as: `a|g` rather...
Currently in formulaic during materialization, bare literals will be silently dropped. In some sense, this makes sense, but given that these are extraneous, an error should be raised. For example:...
#34 Adds support for random effects via the pipe operator. While going through the example in the PR I noticed that operations that are written as `x2|m` were then printed...
This (draft) PR adds support for outputting of modelling metadata from "stateful transforms", which can be used to store (for example) modelling instructions from the formula itself (such as offsets...
Hi @tomicapretto, I stumbled across your cool [formulae](https://github.com/bambinos/formulae) project recently, and it piqued my interest. One of the primary goals of `formulaic` was to be easily extensible to support new...
In some of my work I am interested in exploring two-stage least-square regression on sparse data, and thus in making Formulaic able to handle it nicely. My plan is to...
Formulaic's support for stateful transforms is underutilised. Over the coming couple of weeks I'll be adding support in for the missing transforms, aiming to be compatible with patsy and R...
Has missing value handling been implemented? If so, is there any way to configure what to do? I would like to examine moving linearmodels to formulaic since I would really...
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A high-performance implementation of Wilkinson formulas for Python.