Here is a list of all topics with brief descriptions:

[detail level 12]

Auto-Associative Models.	The project AAM provides classes and tools for unsupervised learning and data analysis using Auto-Associative models
Algebra	The Algebra project provides structures, tools and methods of the usual algebra techniques
Special functions tools	In this project we compute usual and special functions
▼Arrays and Expressions	The Arrays project provides two kinds of arrays for storing in a two entries arrays (matrices) numeric data
Slicing Visitors	A slicing visitor is applied on each column/row of an Expression or Array by using global functions in the STK domain space
Classification	The project Classification propose a set of classes for implementing classifiers
Clustering using generative models	The project Clustering provides classes for modeling and estimating generative mixture model
Data Management	The DManager project propose classes and functions for managing the data
Input-Output	The project InOut propose a set of classes for performing usual input/output operations
Dimension Reduction.	The project Reduct propose a set of classes for computing dimension Reduction (or feature extraction) of a data set
Regression	The project Regress proposes a set of classes for computing usual linear and non-linear regressions
Basis	The project Basis proposes a set of classes for computing function basis
▼Software Development Kit.	The Sdk project propose a set of high level interfaces, template for meta-programming and macros that are used throughout the STK++ projects
Arithmetic properties.	These classes extend the `numeric_limits` C++ struct
Runtime Type Identification.	These classes allow to handle the Runtime type identification (RTTI) problem and are useful when working with heterogeneous data
I/O stream declarations	Nearly all of the I/O classes are parameterized on the type of characters they read and write (The major exception is ios_base at the top of the hierarchy)
▼STatistiK (Statistical tools).	The StatistiK project contains the main tools for computing the usual statistics
The probabilities laws sub-project.	In this sub-project, we compute and simulate the usual probabilities laws: normal law, binomial law, Cauchy law,..
The descriptive statistics sub-project.	In this sub-project, we compute the usual descriptive statistics of variables
Positive kernels.	In this sub-project, we compute the usual positive kernels used by rkhs methods
Statistical Models	The project Model proposes classes for modeling and estimating Statistical Models
▼Kernel tools	The STKernel project is the low-level core library that forms the basis of the project
Fundamental data types	In this subproject we define the fundamental types
Functors	In the Functors subproject, we implement the main functors that can be used throughout the STK++ project, especially in the Arrays project
Hidden implementation details