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personalized2part

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

v0.0.2 · Jan 13, 2026 · GPL (>= 2)

Description

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.

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r-devel-linux-x86_64-fedora-gcc OK
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r-devel-windows-x86_64 OK
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r-patched-linux-x86_64 OK
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NOTE r-oldrel-macos-arm64

installed package size

installed size is 11.3Mb
  sub-directories of 1Mb or more:
    libs  11.0Mb
NOTE r-oldrel-macos-x86_64

installed package size

installed size is 11.4Mb
  sub-directories of 1Mb or more:
    libs  11.1Mb

Check History

NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
NOTE r-oldrel-macos-arm64

installed package size

installed size is 11.3Mb
  sub-directories of 1Mb or more:
    libs  11.0Mb
NOTE r-oldrel-macos-x86_64

installed package size

installed size is 11.4Mb
  sub-directories of 1Mb or more:
    libs  11.1Mb

Dependency Network

Dependencies Reverse dependencies personalized HDtweedie Rcpp foreach personalized2part

Version History

new 0.0.2 Mar 10, 2026
updated 0.0.2 ← 0.0.1 diff Jan 12, 2026
new 0.0.1 Sep 9, 2020