easyml

A toolkit for easily building and evaluating machine learning models.

R tutorial: https://ccs-lab.github.io/easyml/

Installation

You can install the latest development version from CRAN:

install.packages("easyml")

Or from GitHub with:

if (packageVersion("devtools") < 1.6) {
  install.packages("devtools")
}
devtools::install_github("CCS-Lab/easyml", subdir = "R")

If you encounter a clear bug, please file a minimal reproducible example on github.

Examples

Load the easyml library:

library(easyml)

For a dataset with a continuous dependent variable:

data("prostate", package = "easyml")
results <- easy_glmnet(prostate, "lpsa")

For a dataset with a binary dependent variable:

data("cocaine_dependence", package = "easyml")
results <- easy_glmnet(cocaine_dependence, "diagnosis", 
                       family = "binomial", exclude_variables = c("subject", "age"), 
                       categorical_variables = c("male"))

Citation

A whitepaper for easyml is available at https://doi.org/10.1101/137240. If you find this code useful please cite us in your work:

@article {Hendricks137240,
    author = {Hendricks, Paul and Ahn, Woo-Young},
    title = {Easyml: Easily Build And Evaluate Machine Learning Models},
    year = {2017},
    doi = {10.1101/137240},
    publisher = {Cold Spring Harbor Labs Journals},
    URL = {http://biorxiv.org/content/early/2017/05/12/137240},
    journal = {bioRxiv}
}

References

Hendricks, P., & Ahn, W.-Y. (2017). Easyml: Easily Build And Evaluate Machine Learning Models. bioRxiv, 137240. http://doi.org/10.1101/137240