MultiMlearn

The goal of MultiMlearn is to provide a computing approach to estimate individualized treatment rules under the setting of multicategory treatments. This package is designed for data from observational studies (such as electronic health records), but it can also be used for randomized controlled trials.

Installation

This package is under development, so please install the latest version from Github:

if (!requireNamespace("devtools")) {
  install.packages("devtools")
}
devtools::install_github("jitonglou/MultiMlearn")

The first time installation may need to restart the R session several times to install/update some required packages and can take up to 30 minutes. Alternatively, if you do have difficulties in the installation, you can clone this repository to your device, and manually read the functions in R:

files = list.files("./R", full.names = TRUE)
for (file in files){source(file)}

Vignette

An implementation of the MultiMlearn package to a simulated dataset can refer to this website and this R Markdown file. Details of the simulation study can be found in Section S.1 of this pdf document.

Help files of functions

The help files of functions can be found on this website. Also, after you successfully install the package, you can access the help files of some main functions by:

library(MultiMlearn)
?simulate_data
?rfcv2
?mlearn.wsvm.cv

Otherwise, you can read the annotations in the source files: simdata.R, rfcv2.R, and mlearn.R.