penalized

What it is

The penalized package provides methods for performing L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in generalized linear models (GLMs) and the Cox model, aiding in statistical modeling with regularization techniques.

penalized stands out for its comprehensive support of penalization techniques in GLMs and Cox models, making it a powerful tool for researchers and statisticians working with R.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited language support

   The penalized package is primarily developed for R, limiting its accessibility to developers who prefer or are more comfortable with other programming languages.

2. 02

   Poor documentation and examples

   Documentation lacks comprehensive tutorials and real-world usage examples, making it difficult for new users to understand how to effectively use the package's features.

3. 03

   Performance issues with large datasets

   The penalized package can experience slow performance when handling very large datasets, which may limit its usability in big data applications.

What it does well

• Support for L1 and L2 penalized estimation in GLMs

• Cox model support with regularization techniques

• Fused lasso method for feature selection

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.