/**
@mainpage
MADlib is an open-source library for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical and machine learning methods for structured and unstructured data.

The MADlib mission: to foster widespread development of scalable analytic skills, by harnessing efforts from commercial practice, academic research, and open-source development.

MADlib front page: http://madlib.net/

@defgroup grp_modeling Data Modeling

    @defgroup grp_suplearn Supervised Learning
    @ingroup grp_modeling

		@defgroup grp_bayes Naive Bayes Classification
		@ingroup grp_suplearn

		@defgroup grp_linreg (Multi-)Linear Regression
		@ingroup grp_suplearn
		
		@defgroup grp_logreg Logistic Regression
		@ingroup grp_suplearn

        @defgroup grp_kernmach Support Vector Machines
		@ingroup grp_suplearn

    @defgroup grp_unsuplearn Unsupervised Learning
    @ingroup grp_modeling

        @defgroup grp_kmeans k-Means Clustering
        @ingroup grp_unsuplearn

        @defgroup grp_svdmf SVD Matrix Factorisation
        @ingroup grp_unsuplearn

@defgroup grp_desc_stats Descriptive Statistics

    @defgroup grp_sketches Sketch-based Estimators
    @ingroup grp_desc_stats
    
        @defgroup grp_countmin CountMin (Cormode-Muthukrishnan)
        @ingroup grp_sketches
        
        @defgroup grp_fmsketch FM (Flajolet-Martin)
        @ingroup grp_sketches

        @defgroup grp_mfvsketch MFV (Most Frequent Values)
        @ingroup grp_sketches
    
    @defgroup grp_profile Profile 
    @ingroup grp_desc_stats
    
    @defgroup grp_quantile Quantile
    @ingroup grp_desc_stats

@defgroup grp_support Support Modules

    @defgroup grp_svec Sparse Vectors
    @ingroup grp_support
*/