Package: personalized2part 0.0.2

personalized2part: Two-Part Estimation of Treatment Rules for Semi-Continuous Data

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.

Authors:Jared Huling [aut, cre]

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Peer review:

Bug tracker:https://github.com/jaredhuling/personalized2part/issues

On CRAN:

6 exports 2 stars 0.83 score 81 dependencies 5 scripts 157 downloads

Last updated 4 years agofrom:c1f86b3c38. Checks:OK: 1 NOTE: 8. Indexed: yes.

Exports:cv.hd2partfit_subgroup_2parthd2parthdgammaHDtweedie_kfold_augsim_semicontinuous_data

Dependencies:askpassbase64encbslibcachemclicodetoolscolorspacecpp11crosstalkcurldata.tabledigestdplyrevaluatefansifarverfastmapfontawesomeforeachfsgenericsggplot2glmnetgluegtableHDtweediehighrhtmltoolshtmlwidgetshttrisobanditeratorsjquerylibjsonlitekernlabknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmeopensslpersonalizedpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownsassscalesshapestringistringrsurvivalsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxgboostyaml