penalized
L1 and L2 penalized estimation in GLMs and Cox models for R.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is penalized?
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.
Key differentiator
“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.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The penalized package is primarily developed for R, limiting its accessibility to developers who prefer or are more comfortable with other programming languages.
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.
The penalized package can experience slow performance when handling very large datasets, which may limit its usability in big data applications.
Fit analysis
Who is it for?
✓ Best for
Researchers needing to apply L1 and L2 penalties in GLMs for feature selection
Teams working on survival analysis who require regularization techniques
Academics studying statistical methods with R
✕ Not a fit for
Projects requiring real-time data processing or streaming analytics
Applications that do not involve statistical modeling or machine learning
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Next step
Get Started with penalized
Step-by-step setup guide with code examples and common gotchas.