Implements the methodology of Huling, Smith, and Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part modeling framework to handle semi-continuous data by separately modeling the positive part of the outcome and an indicator of whether each outcome is positive, but still results in a single treatment rule. High dimensional data is handled with a cooperative lasso penalty, which encourages the coefficients in the two models to have the same sign.
Package details |
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Maintainer | |
License | GPL (>= 2) |
Version | 0.0.2 |
URL | https://github.com/jaredhuling/personalized2part |
Package repository | View on GitHub |
Installation |
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