c(
person("Jared", "Huling",  role = c("aut", "cre"), email = "jaredhuling@gmail.com"),
person("Aaron", "Potvien", role = "ctb")
)
Jared Huling [aut, cre],
Aaron Potvien [ctb]

glmnet (>= 2.0-13), mgcv, gbm, ggplot2, plotly

c(
person("Jared", "Huling",  role = c("aut", "cre"), email = "jaredhuling@gmail.com"),
person("Aaron", "Potvien", role = "ctb"),
person("Alexandros", "Karatzoglou", role = "cph"),
person("Alex", "Smola", role = "cph")
)
Provides functions for fitting and validation of models for subgroup
identification and personalized medicine / precision medicine under the general subgroup
identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>.
This package is intended for use for both randomized controlled trials and
observational studies.
Jared Huling [aut, cre],
Aaron Potvien [ctb],
Alexandros Karatzoglou [cph],
Alex Smola [cph]

c(
person("Jared", "Huling",  role = c("aut", "cre"), email = "jaredhuling@gmail.com",
comment = c(ORCID = "0000-0003-0670-4845")),
person("Aaron", "Potvien", role = "ctb"),
person("Alexandros", "Karatzoglou", role = "cph"),
person("Alex", "Smola", role = "cph")
)
Jared Huling [aut, cre] (<https://orcid.org/0000-0003-0670-4845>),
Aaron Potvien [ctb],
Alexandros Karatzoglou [cph],
Alex Smola [cph]

Provides functions for fitting and validation of models for subgroup
identification and personalized medicine / precision medicine under the general subgroup
identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>.
This package is intended for use for both randomized controlled trials and
observational studies and is described in detail in Huling and Yu (2021)
<doi:10.18637/jss.v098.i05>.

survival, methods, kernlab, foreach, xgboost
glmnet (>= 2.0-13), mgcv, ggplot2, plotly

Two-Part Estimation of Treatment Rules for Semi-Continuous Data
c(
person("Jared", "Huling",  role = c("aut", "cre"), email = "jaredhuling@gmail.com",
comment = c(ORCID = "0000-0003-0670-4845"))
)
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.