pgmpy
generalized-additive-models
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| pgmpy | generalized-additive-models | |
|---|---|---|
| 2 | 1 | |
| 2,617 | 13 | |
| 1.4% | - | |
| 8.0 | 7.0 | |
| 6 days ago | 3 months ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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pgmpy
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Use data from tables generated in python console,
No need to post the help, here is the DiscreteFactor class https://github.com/pgmpy/pgmpy/blob/eb65f40d2b32bf2ad971181333bb9ed7aefde907/pgmpy/factors/discrete/DiscreteFactor.py
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[D] Python toolboxes for probabilistic graphical model inference
I do know of a few promising toolboxes such as pgmpy, pymc3, and pyro, but have not used either of them (for this purpose) and am at a bit of a loss picking one to start with.
generalized-additive-models
What are some alternatives?
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
pyhf - pure-Python HistFactory implementation with tensors and autodiff
statsmodels - Statsmodels: statistical modeling and econometrics in Python
pandas-profiling - Create HTML profiling reports from pandas DataFrame objects [Moved to: https://github.com/ydataai/pandas-profiling]
scikit-learn - scikit-learn: machine learning in Python
CausalPy - A Python package for causal inference in quasi-experimental settings
rustworkx - A high performance Python graph library implemented in Rust.
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
GLM.jl - Generalized linear models in Julia