kernlab vs e1071
Side-by-side — pricing, strengths, tradeoffs, and fit.
kernlab
Kernel-based machine learning lab for R.
e1071
Misc Functions of the Department of Statistics at TU Wien 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
Kernel-based machine learning lab for R.
One-liner
Misc Functions of the Department of Statistics at TU Wien 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
Data scientists working with R who need advanced machine learning techniques such as kernel methods for classification and regression tasks.
Researchers looking to perform spectral clustering on complex datasets.
Developers needing quantile regression capabilities in their R projects.
✓ Best for
Researchers needing a comprehensive set of tools for classification tasks within the R environment.
Statisticians who require SVM and fuzzy clustering functionalities.
Not ideal for
✕ Not for
Projects requiring real-time processing or low-latency responses, as kernlab is designed more for batch processing.
Users who prefer a graphical user interface (GUI) over command-line interfaces and programming.
✕ Not for
Developers looking for cloud-based machine learning services
Teams requiring real-time data processing capabilities
Strengths
↑ Strengths
Support for various kernel methods including SVM, kPCA, and spectral clustering.
Quantile regression support.
Dimensionality reduction techniques.
Novelty detection algorithms.
↑ Strengths
Support Vector Machines (SVMs)
Short Time Fourier Transform
Fuzzy Clustering
Naive Bayes Classifier
Weaknesses
↓ Weaknesses
Limited documentation and examples
Performance issues with large datasets
Limited support for non-R languages
↓ Weaknesses
Limited support for modern R features
Poor documentation quality
Performance issues with large datasets