The Statistical ToolKit Project

Presentation

STK++ is a multi-platform toolkit written in C++ for creating fast and easy to use Statistics and/or data mining console programs.
STK++ is a versatile, fast, reliable and elegant collection of C++ classes for statistics, clustering, linear algebra, arrays (with an Eigen-like API), regression, dimension reduction, etc. Some functionalities provided by the library are available in the R environment as R functions. The library offers a dense set of (mostly) template classes in C++ and is suitable for projects ranging from small one-off projects to complete data mining application suites.

All STK++ programs are free software; you can redistribute them and/or modify them under the terms of the GNU Lesser (Library) General Public License as published by the Free Software Foundation.

The licensing terms for STK++ , the GNU LGPL, allow it to be used by all developers, including those developing proprietary software, without any license fees or royalties.

The STK++ team use the eclipse platform with the cdt plugin.

Tutorials

Table of contents

• Tips
  • Tips: Centering an array
  • Tips: Compute the mean for each column of an array
  • Tips: Compute the mean and the variance of multidimensionnal data
• The Arrays And Their Usages
  • Arrays Tutorial 1 : Introduction to STK++ Arrays and their usages
  • Arrays Tutorial 2 : The Constructors
  • Arrays Tutorial 3 : Using Array2D
  • Arrays Tutorial 4 : Using Visitors, Appliers and Functors
  • Arrays Tutorial 5 : Slicing arrays and expressions
  • Arrays Tutorial 6 : Using reference Arrays
• Other tutorials
  • Use of the Algebra utilities
  • Description of the STKernel projects: The Kernel tools.
  • STatistiK: Tools for usual statistics
  • Clustering: How to add new mixture models (Part 1)
  • A STK++ application example: the MixBin program

Projects

The STK++ project is divided in different projects, each of them being (eventually) divided into subproject:

• STKernel: Kernel tools
• Sdk: Software Development Kits
• Arrays: Containers oriented to numerical computation
• DManager: Tools for handling data and input/output
• Analysis: Special functions and template algorithms
• Algebra: Linear Algebra tools
• STatistiK: Statistical Tools
• StatModels: Statistical Models
• Classification: classification algorithms
• Clustering: Mixture Models
• Reduct: Dimension reduction tools
• Regress: Regression tools
• AAModels: Auto-Associative Models