Python package for targeted inference.

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Targeted Learning Library

Python package for targeted inference.

targeted provides a number of methods for semi-parametric estimation. The library also contains implementations of various parametric models (including different discrete choice models) and model diagnostics tools.

The implemention currently includes

  • Risk regression models with binary exposure (Richardson et al., 2017, doi:10.1080/01621459.2016.1192546)
  • Augmented Inverse Probability Weighted estimators for missing data and causal inference (Bang and Robins, 2005, doi:10.1111/j.1541-0420.2005.00377.x)
  • Model diagnostics based on cumulative residuals methods
  • Efficient weighted Pooled Adjacent Violator Algorithms
  • Nested multinomial logit models

Documentation and tutorials can be found at https://targetlib.org/python/.

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0.0.35

0.0.34

0.0.32

0.0.31

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0.0.30

0.0.29

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0.0.26

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  • Download URL: targeted-0.0.30-cp36-cp36m-macosx_10_9_x86_64.whl
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  • Size: 287.2 kB
  • Tags: CPython 3.6m, macOS 10.9+ x86-64
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  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

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