Advanced Machine Learning Solutions

Specializing in Random Forest Implementations, Variable Selection, and OpenMP Parallel Processing

Our Open Source Packages

High-performance machine learning tools for researchers and developers

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randomForestSRC

Fast Unified Random Forests for Survival, Regression, and Classification: This comprehensive package supports multiple analysis types including survival analysis, competing risks, multivariate regression, and class imbalanced classification. Features extreme random forests, advanced imputation methods, and innovative variable importance measures.

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randomForestSGT

Super Greedy Trees for high-dimensional data analysis: This package features innovative splitting techniques including CART splits, hyperplane, ellipsoid and hyperboloid cuts. Uses coordinate descent for fast penalized lasso calculations and best split first (BSF) construction for optimal empirical risk reduction.

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varPro

Model-Independent Variable Selection via Rule-Based Variable Priority. VarPro addresses bias in traditional permutation importance by using observed data comparisons within rule regions. This innovative approach provides robust variable selection for regression, classification, and survival analysis without artificial data manipulation.

Our Machine Learning Products

Comprehensive solutions for your data analysis needs

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randomForestSRC

Fast Unified Forests

Key Features

  • Unified framework for survival, regression, and classification
  • High-performance implementation in R
  • Extensive support for missing data
  • Variable importance and partial dependence plots
  • Competitive prediction accuracy

Technical Highlights

  • Efficient C++ backend
  • Parallel processing support
  • Comprehensive documentation
  • Active development and maintenance
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randomForestSGT

Super Greedy Trees

Key Features

  • Optimized for high-dimensional data
  • Greedy splitting algorithm for improved performance
  • Enhanced variable selection
  • Robust to noise and outliers
  • Compatible with standard randomForest interfaces

Technical Highlights

  • Efficient memory management
  • Fast training times
  • Comprehensive test suite
  • Detailed vignettes and examples
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varPro

Variable selection via the rule based priority

Key Features

  • Rule-based variable selection
  • Interpretable machine learning
  • Works with high-dimensional data
  • Flexible priority rules
  • Integration with common ML workflows

Technical Highlights

  • Efficient implementation
  • Comprehensive visualization tools
  • Detailed case studies
  • Active user community

About Kogalur and Company

We are a software engineering company specializing in machine learning and artificial intelligence solutions. Our team combines deep theoretical knowledge with practical software engineering expertise to create robust, high-performance tools for data analysis.

Our Mission

To deliver cutting-edge machine learning tools that bridge the gap between academic research and industrial applications. We focus on creating software that is not only theoretically sound but also practical and easy to use in real-world scenarios.

Our Expertise

With years of experience in random forest methodologies and variable selection techniques, we've developed a suite of open-source tools that are widely used in both research and industry. Our packages are known for their performance, reliability, and comprehensive documentation.

Open Source Commitment

We believe in the power of open-source software to advance scientific research and democratize access to advanced machine learning tools. All of our packages are freely available and actively maintained.

Get in Touch

Contact Us

Have questions about our products or interested in collaboration? Reach out to us.

Email us directly at:

ubkogalur@gmail.com

Or send us a message