Two-Part Estimation of Treatment Rules for Semi-Continuous Data

Implements the methodology of Huling, Smith, and Chen (2020) , 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.


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0.0.1 by Jared Huling, a year ago

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Authors: Jared Huling [aut, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, foreach, methods

Depends on personalized, HDtweedie

Linking to Rcpp, RcppEigen

See at CRAN