Pyglmnet: Python implementation of elastic-net regularized generalized linear models
Journal of Open Source Software, 5(47), 1959 · 2020
About this work
Pyglmnet implements generalized linear models with penalties that help control model complexity. Researchers can select a response distribution and regularization approach within a Python workflow, including applications where observations are counts, binary outcomes, or continuous measurements.
Cite this work
Mainak Jas; Titipat Achakulvisut; Aid Idrizović; Daniel Acuna; Matthew Antalek; Vinicius Marques; Tommy Odland; Ravi Garg; Mayank Agrawal; Yu Umegaki; Peter Foley; Hugo Fernandes; Drew Harris; Beibin Li; Olivier Pieters; Scott Otterson; Giovanni De Toni; Chris Rodgers; Eva Dyer; Matti Hamalainen; Konrad Kording; Pavan Ramkumar (2020). Pyglmnet: Python implementation of elastic-net regularized generalized linear models. Journal of Open Source Software, 5(47), 1959. 10.21105/joss.01959.
@article{jas2020pyglmnet,
title = {Pyglmnet: Python implementation of elastic-net regularized generalized linear models},
author = {Jas, Mainak and Achakulvisut, Titipat and Idrizović, Aid and Acuna, Daniel and Antalek, Matthew and Marques, Vinicius and Odland, Tommy and Garg, Ravi and Agrawal, Mayank and Umegaki, Yu and Foley, Peter and Fernandes, Hugo and Harris, Drew and Li, Beibin and Pieters, Olivier and Otterson, Scott and De Toni, Giovanni and Rodgers, Chris and Dyer, Eva and Hamalainen, Matti and Kording, Konrad and Ramkumar, Pavan},
year = {2020},
publication_date = {2020-03-01},
journal = {Journal of Open Source Software},
volume = {5},
number = {47},
pages = {1959},
doi = {10.21105/joss.01959},
url = {https://joss.theoj.org/papers/10.21105/joss.01959}
}