BavoDC/ssmurf: Sparse Multi-Type Regularized Feature Modeling (package for testing purposes)
Adjusted version of the original smurf package. Hopefully the extra s in ssmurf stands for speedy and not slow. The original package is an implementation of the SMuRF algorithm of Devriendt et al. (2021) <doi:10.1016/j.insmatheco.2020.11.010> to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood.
Getting started
Browse package contents
Package details |
|
|---|---|
| Author | Tom Reynkens [aut, cre] (<https://orcid.org/0000-0002-5516-5107>), Sander Devriendt [aut], Katrien Antonio [aut], Bavo DC Campo [aut] |
| Maintainer | Tom Reynkens <tomreynkens@hotmail.com> |
| License | GPL (>= 2) |
| Version | 1.1.2 |
| URL | https://gitlab.com/TReynkens/smurf |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
|
