In JSzitas/ridges:

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

ridges

About

Work in Progress

A tiny ridge regression library for multiple targets, with optional automatic selection of the ridge \code{\math{\lambda}} parameter (Using the estimator 'k36' from this paper (see references), for all, not only Poisson regression cases. )

Obviously this is highly experimental and has no mathematical guarantees... but I try to have a curated set of benchmarks to showcase whether it works in practice.

The goal here is absolutely NOT to provide an interface like glm or glmnet, but rather to have a lean, easy to understand set of solvers, with models capable of producing predictions. These solvers should be easy to plug-in where-ever you need to solve a ridge regression problem, and not much else.

It also serves as a simple educational tool - since the implementation is primarily concerned with simplicity and does no unnecessary calculations, it can be used to reasonably showcase ridge regression in a generalized linear model context. While elastic-net could be considered a better approach in many contexts, I believe most implementations are opaque and hard to understand.

To the goal of simplicity, this package currently supports no scaling/re-scaling, no addition of intercepts, no formulas, and absolutely no offsets. The scaling and intercepts might be added at some point, but I would prefer to keep this contained as a very bare-bones implementation.