- Normal mixture models
- Several codes are available that characterize multivariate
datasets as mixtures of Gaussian populations via likelihood methods, often using
Bayesian principles. They include:
EMMIX
by G. McLachlan
**P**, MCLUST by C. Fraley and A. Raftery**P**, AutoClass C by P. Cheeseman**P**, and Snob by D. Dowe.**P** - Multivariate data analysis software
- Collection of subroutines for principal components analysis, partitioning, hierarchical clustering. discriminant analyses (linear, multiple, k-nearest neighbors), correspondence analysis, multidimensional scaling, Sammon mapping, Kohonen self-organizing feature map.
- Classification Society of North America (CSNA)
- Metasite with many links to classification meetings, journals, discussion groups, commercial and on-line software.
- Software for clustering and multivariate analysis
- Metasite with descriptions of on-line programs and packages.
- Machine Learning Library in C++ (MLC++)
- Data mining and multivariate classification package including
data manipulation, variety of categorizers (on attributes, thresholds, nearest
neighbor, perceptron, decision tree ), induction algorithms, and visualization
tools of data and trees.
**(P)** - R Package
- Package in Pascal developed for ecological spatio-temporal
multivariate datasets based on monograph by L. & P. Legendre (1983).
Functionalities include autocorrelation using correlograms (Moran's I
and Geary's c indices), hierarchical agglomerative clustering, k-means
clustering, chronological clustering for multivariate time series,
analysis of variance, geometrical connectors, (nearest neighbor,
Gabriel's connection, Delaunay triangulation), Mantel's two-sample
statistic, multidimensional scaling by principal coordinates analysis,
univariate periodogram.
**(P)** - ADE-4
- Multivariate analysis and graphical display package for Macintosh and Windows 95.
Also provided is NetMul, a Web interface to ADE-4 for on-line principal components
analysis, co-inertial analysis and discriminant analysis.
**(P)** - Feasible solution algorithms
- Algorithms for the common high breakdown estimation criteria, and to
find the minimum volume ellipsoid in multivariate datasets.
**(P)** - IPP
- Interactive Projection Pursuit, providing 1- and 2-dimensional
projections of multivariate data for interactive discovery of structure.
The user chooses and graphically investigates interesting projections.
From Case Western Reserve University. C and Fortran algorithms
installed as a library for S-Plus.
**(P)** - Oblique decision trees
- Hyperplane partitioning of multivariate datasets
**(P)** - Dysect
- Clustering algorithm based on dynamic altering of hierarchies.
**(P)** - Fast Algorithm for Classification Trees"
- Tree-structures classification similar to CART.
**(P)** - Cluster
- Library of several dozen subroutines from NIST for multivariate clustering algorithm from 1975 monobraph by J. A. Hartigan.
- Cluster analysis
- Six programs computing dissimilarities, partitioning using medoids, k-medoid clustering, fuzzy clustering, agglomerative and divisive hierarchical clustering, clustering of binary data.
- CLUSBAS
- Average-linkage hierarchical clustering.
- Hierarchical clustering
- Algorithm for agglomerative clustering using various criteria (Ward's minimum variance, single linkage, average linkage, complete linkage, McQuitty's method, median method, centroid method).
- AS 15 ,
- Algorithm for single-linkage and minimum intra-cluster variance clustering.
- AS 58
- Algorithm for single-linkage and minimum intra-cluster variance clustering.
- k-means clustering ,
- k-means clustering minimizing intra-cluster variance.
- Multivariate linear regression by least median of squares.
- Minimum volume ellipsoid estimator
- Robust estimator of multivariate location and dispersion.
- Hypo
- Hypothesis testing for means and spreads for multivariate Gaussian data.
- Projection pursuit
- Two-dimensional exploratory projection pursuit.
- Multivariate skewness and kurtosis
- Probabilities of R^2
- Distribution function of the square multiple correlation coefficient
- Linear dependency analysis for multivariate data.
- Principal components analysis

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