person("Jared", "Huling",  role = c("aut", "cre"), email = "jaredhuling@gmail.com"),
person("Aaron", "Potvien", role = "ctb")
Depends: glmnet, mgcv, gbm, ggplot2, plotly

Imports: survival, kernlab, methods, foreach

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

Imports: survival, methods, kernlab, foreach

person("Aaron", "Potvien", role = "ctb"),
person("Alexandros", "Karatzoglou", role = "cph"),
Description: Provides functions for fitting and validation of models for subgroup
identification and personalized medicine / precision medicine under the general subgroup

person("Jared", "Huling",  role = c("aut", "cre"), email = "jaredhuling@gmail.com",

Suggests: knitr, rmarkdown, testthat, nnet

observational studies and is described in detail in Huling and Yu (2021)

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

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

Description: Functions to setup a personal R package that
attaches given libraries and exports personal helper functions.
Imports: desc, devtools, fs, glue, magrittr, purrr, rprojroot,

Description: Functions to setup a personal R package that attaches given
libraries and exports personal helper functions.

Title: Pictographic Representation of Treatment Effects
Author: Joel Kuiper [aut, cre], Iain Marshall [ctb]
Authors@R: c(person("Joel", "Kuiper", email = "jkuiper@doctorevidence.com", role = c("aut", "cre")), person("Iain", "Marshall", email = "iain.marshall@kcl.ac.uk", role = c("ctb")))
Description: Visualizes treatment effects using person icons, similar to Cates (NNT) charts.
Packaged: 2015-09-21 14:19:01 UTC; joelkuiper