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.
|Imports:||Rcpp, foreach, methods|
|Author:||Jared Huling [aut, cre]|
|Maintainer:||Jared Huling <jaredhuling at gmail.com>|
|License:||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]|
|Citation:||personalized2part citation info|
|CRAN checks:||personalized2part results|
|Windows binaries:||r-devel: personalized2part_0.0.1.zip, r-release: personalized2part_0.0.1.zip, r-oldrel: personalized2part_0.0.1.zip|
|macOS binaries:||r-release (arm64): personalized2part_0.0.1.tgz, r-oldrel (arm64): personalized2part_0.0.1.tgz, r-release (x86_64): personalized2part_0.0.1.tgz, r-oldrel (x86_64): personalized2part_0.0.1.tgz|
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