glmpath
L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is glmpath?
glmpath is an R package that provides a path of L1 regularized solutions to generalized linear models and Cox proportional hazards model, aiding in feature selection and model fitting with regularization.
Key differentiator
“glmpath stands out as an R package specifically designed to provide a path of L1 regularized solutions, making it ideal for feature selection in generalized linear models and survival analysis.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
glmpath requires a strong understanding of statistical concepts such as L1 regularization, generalized linear models, and Cox proportional hazards model.
The package is only available in R, which can be a limitation for teams that prefer or are already using other programming languages like Python or Julia.
As an open-source project focused on specialized statistical methods, the user base is relatively small, leading to limited community contributions and slower issue resolution.
glmpath can be computationally intensive for very large datasets, which may lead to long processing times or memory issues in R.
Fit analysis
Who is it 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.
✕ Not a fit for
Developers looking for machine learning libraries with extensive model types beyond GLMs and Cox Models
Users requiring cloud-based or API-accessible services
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 glmpath
Step-by-step setup guide with code examples and common gotchas.