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/.
1 maintainer
Metadata
1 maintainer
Release files for targeted 0.0.35
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| targeted-0.0.35.tar.gz | 39.8 kB | Without provenance | Details for targeted-0.0.35.tar.gz |
Built distributions (wheels)
Total release size: 48.7 MB
Release files / targeted-0.0.35.tar.gz
| Download URL | targeted-0.0.35.tar.gz |
|---|---|
| Size | 39.8 kB |
| Tags | Source |
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