Package for L1 and L2 penalized estimation of matrix linear models (bilinear models for matrix-valued data).
MatrixLMnet depends on the MatrixLM package,
which provides core functions for closed-form least squares estimates for matrix linear models.
See the paper, "Sparse matrix linear models for structured high-throughput data", and its reproducible code for details on the L1 penalized estimation.
The MatrixLMnet package can be installed by running:
using Pkg
Pkg.add("MatrixLMnet")
For the most recent version, use:
using Pkg
Pkg.add(url = "https://github.com/senresearch/MatrixLMnet.jl", rev="main")
Alternatively, you can also install MatrixLMnet from the julia REPL. Press ] to enter pkg mode again, and enter the following:
add MatrixLMnet
We appreciate contributions from users including reporting bugs, fixing issues, improving performance and adding new features.
If you have questions about contributing or using MatrixLMnet package, please communicate with authors form github.
If you use MatrixLMnet in a scientific publication, please consider citing following paper:
Jane W. Liang. Śaunak Sen. "Sparse matrix linear models for structured high-throughput data." Ann. Appl. Stat. 16 (1) 169 - 192, March 2022. https://doi.org/10.1214/21-AOAS1444
@article{10.1214/21-AOAS1444,
author = {Jane W. Liang and Śaunak Sen},
title = {{Sparse matrix linear models for structured high-throughput data}},
volume = {16},
journal = {The Annals of Applied Statistics},
number = {1},
publisher = {Institute of Mathematical Statistics},
pages = {169 -- 192},
keywords = {ADMM, FISTA, gradient descent, Julia, Lasso, proximal gradient algorithms},
year = {2022},
doi = {10.1214/21-AOAS1444},
URL = {https://doi.org/10.1214/21-AOAS1444}
}
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