The GEM package provides tools for simulating data and fitting a GEM model using Torch in R. It includes functions to generate synthetic datasets, fit a model based on those data, and assess model performance.
Install with:
renv::install("weinstockj/GEM")Alternatively, clone the repository and install the package in R:
devtools::install()Use simulate_data to generate a synthetic dataset:
This function returns a list containing:
- x: a numeric matrix of predictors with one row for each candidate somatic mutation and one column for each feature
- y: a numeric response vector containing zcore'd age at blood draw
- z: an indicator for whether a candidate mutation is a "real"
- sample_map: a list mapping samples to indices
- true_count and naive_count for model assessment
Fit the GEM model on your data using the fit_model function:
model <- fit_model(x, y, sample_map, lr = 0.1, iters = 180, num_threads = 4L)The returned list includes:
- Model coefficients (e.g. beta),
- Fitted mutation burden estimates (fitted),
- Loss history, and other parameters.
To run the tests for GEM, execute:
devtools::test()Please cite our manuscript if you use this package in your research.