Welcome to the home of RobustiPy
, a library for the creation of a more robust and stable model space. Kindly note: this project is in the early stages of development. Its functionally and API might change without notice!
RobustiPy
performs Multiversal/Specification Curve Analysis. Multiversal/Specification Curve Analysis attempts to compute most or all reasonable specifications of a statistical model, understanding a specification as a single attempt to estimate an estimand of interest, whether through a particular choice of covariates, hyperparameters, data cleaning decisions, and so forth.
More formally, lets assume we have a general model of the form:
We are essentially attempting to model a dependent variable
RobustiPy
will then:
In words, it creates a set contaning the aritmentic mean of the elements of the powerset RobustiPy
then takes these specifications, fits them against observable (tabular) data, and produces coefficients and relevant metrics for each version of the predictor
To install directly (in Python
) from GitHub, run:
git clone https://github.com/RobustiPy/robustipy.git
cd robustipy
pip install .
In a Python script (or Jupyter Notebook), import the OLSRobust
class by running:
from robustipy.models import OLSRobust
model_robust = OLSRobust(y=y, x=x, data=data)
model_robust.fit(controls=c,
draws=100,
sample_size=100)
model_results = model_robust.get_results()
Where y
is a list of variable names used to create your dependent variable, and x
is a list of variable names used as predictors.
A working usage example script -- replication_example.py
-- is provided at the root of this repository. You can also find a number of empirical examples here and some simulated examples here.
We have a website made with jekkyl-theme-minimal that you can find here. It also contains details of a Hackathon!
Please kindly see our guide for contributors file as well as our code of conduct. If you would like to become a formal project maintainer, please simply contact the team to discuss!
This work is free. You can redistribute it and/or modify it under the terms of the GNU GPL 3.0 license. The two dataset which is bundled with the library comes with it's own licensing conditions, and should be treatedly accordingly.
We are grateful to the extensive comments made by various academic communities over the course of our thinking about this work, not least the members of the ESRC Centre for Care and the Leverhulme Centre for Demographic Science.