glmpath vs penalized
Side-by-side — pricing, strengths, tradeoffs, and fit.
glmpath
L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model.
penalized
L1 and L2 penalized estimation in GLMs and Cox models for R.
At a glance
Open SourceFree Tier
· Not enough data·From $0
Open SourceFree Tier
· Not enough data·From $0
Overview
One-liner
One-liner
L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model.
One-liner
L1 and L2 penalized estimation in GLMs and Cox models for R.
Maturity
Maturity
Not enough dataMaturity
Not enough dataOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableOpen source — free to use
Starts at
Starts at
$0
Starts at
$0
Model
Model
Flat rate
Model
Flat rate
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Researchers needing L1 regularization for feature selection in generalized linear models and survival analysis.
Academics working on statistical modeling who require a comprehensive path of solutions.
✓ 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 ideal for
✕ Not for
Developers looking for machine learning libraries with extensive model types beyond GLMs and Cox Models
Users requiring cloud-based or API-accessible services
✕ Not for
Projects requiring real-time data processing or streaming analytics
Applications that do not involve statistical modeling or machine learning
Strengths
↑ Strengths
L1 Regularization Path for Generalized Linear Models
Supports Cox Proportional Hazards Model
Feature Selection via Regularization
Path of Solutions for Different Regularization Parameters
↑ Strengths
Support for L1 and L2 penalized estimation in GLMs
Cox model support with regularization techniques
Fused lasso method for feature selection
Weaknesses
↓ Weaknesses
Steep learning curve for non-statisticians
Limited to R language
Sparse community and support
Performance limitations with large datasets
↓ Weaknesses
Limited language support
Poor documentation and examples
Performance issues with large datasets