 CANPLS  Performs canonical correlation and partial leastsquares analyses. Used to study correlations between two sets of variables.

 CONSEN  Computes a consensus tree for two of two or more trees (such as multiple tied trees from SAHN or between two different methods). Several consensus indices are also computed to measure the degree of agreement between trees.

 COPH  Produces a cophenetic value matrix (matrix of ultrametric values) from a tree matrix (produced, e.g., by the SAHN program). This matrix can be used by the MXCOMP program to test the goodness of fit of a cluster analysis to the similarity or dissimilarity matrix on which it was based.

 CORRESP  Correspondence analysis. This is a useful way to investigate the structure of 2way contingency table.

 CPCA  Common principal components analysis of Flury (1984, 1988). Fits a single set of eigenvectors to a series of variancecovariance matrices.

 CVA  Performs a canonical vectors analysis (a generalization of discriminant function analysis). Can also test closeness of each specimen to each group mean.

 DCENTER  Performs a "doublecentering" of a matrix of similarities or dissimilarities among the objects. The resulting matrix can then be factored to perform a principal coordinates analysis (a method for displaying relationships among objects in terms of their positions along a set of axes based on a dissimilarity matrix, see Gower 1966).

 EIGEN  Computes eigenvector and eigenvalue matrices of a real symmetric similarity matrix. This program can be used to perform a principal components or a principal coordinates analysis by extracting eigenvectors (factors) from a correlation or variancecovariance matrix.

 FOURIER  Computes Fourier and elliptic Fourier transformations and their inverses.
Can be used on both 2D and 3D outline curves.

 MDSCALE  Nonmetric and linear multidimensional scaling analysis. This can be used as an alternative to PCA.

 MOD3D  Plots a 3way scatter diagram as a 3D perspective view of a model with n "objects" at tops of wires attached from a base plane. The view can be rotated interactively. This program is often used to view the results of a principal components or principal coordinates analysis.

 MST  Computes a minimumlength spanning tree from a similarity or dissimilarity matrix. This is useful for showing the nearest neighbors of objects based on their positions in a multidimensional space.

 MXCOMP  Compares two symmetric matrices by computing their matrix correlation and then plotting a scatter diagram. The statistics for a 2way Mantel test are also computed. It can be used to compute the goodness of fit of a cluster analysis to a dataset (by comparing a cophenetic value matrix with a dissimilarity matrix).

 MXPLOT  Plots 2way scatter diagrams of rows or columns of a matrix.

 NJOIN  Computes Saitou and Nei's (1987) neighborjoining method trees as estimated phylogenetic trees.

 OUTPUT  Formats matrices into pages for printing. Results can be pasted into most word processors. This formatted output is also useful for checking to make sure that an input file has been prepared in the correct format for NTSYSpc.

 POOLVC  Computes a pooled withingroups variancecovariance matrix from two or more data matrices. Can also perform a test for homogeneity
of covariance matrices.

 PROJ  Projects a set of objects onto one or more vectorsor onto a space orthogonal to a set of vectors. In principal components analysis one will project standardized data onto the eigenvectors of the correlation matrix in order to see the best (in a leastsquares sense) lowdimensional view of a data set. The orthogonal projection option can be used to implement Burnaby's (1966) method for size adjustment.

 SAHN  Performs the sequential, agglomerative, hierarchical, and nested clustering methods as defined by Sneath and Sokal (1973) . These include such commonly used hierarchical clustering methods as listed below. The program can find alternative trees when there are ties in the input matrix.
 completelink (maximum method)
  singlelink (minimum method)
  flexible clustering
  UPGMA (unweighted pairgroup method)
  WPGMA (weighted pairgroup method)
  WPGM using centroid clustering (either similarities or dissimilarities)
  WPGM using Spearman's average


 SIMGEND  Computes matrices of genetic distance coefficients from genefrequency and DNA sequence data. The following coefficients can be selected.
 CavalliSforza and Edwards (1967) arc distance
  Balakrishnan and Sanghvi (1968) distance.
  CavalliSforza and Edwards (1967) chord distance.
  Hillis (1984) distance
  Swofford and Olsen's (1990) suggestion to unbiass the distance by using same correction as in Nei's distance.
  Nei's (1972) distance (default).
  Nei's (1978) unbiased distance. Formula as above but with denominator:
  Prevosti (Wright, 1978 ) distance.
  Rogers (1972) distance
  Rogers distance as modified by Wright (1978
  Jukes and Cantor (1969) distance modified for DNA sequence data.


 SIMINT  Computes various similarity or dissimilarity indices for interval measure (continuous) data (e.g., correlation, distance, etc. coefficients).
 BrayCurtis distance
  Canberra metric
  Chisquared distance
  Average taxonomic distance
  Squared average distance
  Euclidean distance
  Euclidean distance squared
  Manhattan distance
  Penrose's shape coefficient
  Penrose's size coefficient
  Productmoment correlation
  Cosine of angle
  Sample size
  Morisita (1959) index
  Horn's (1966) modification of Morisita index
  Renkonen (1938) similarity
  Variances and covariances
  Inner product


 SIMQUAL  Computes various association coefficients for qualitative data data with unordered states (e.g., simple matching, Jaccard, phi, etc. coefficients).
Hamann (1961) coefficient
 Rogers and Tanimoto (1960) distance
  Simple matching coefficient
  Dice (1945) coefficient
  Jaccard (1908) coefficient
  Kulcznski (1927) coefficients 1 and 2
  Phi coefficient
  Russel and Rao (1940) coefficient
  Ochiai coefficient
  Yule (1911) coefficient
  also several unnamed coefficients from Sokal and Sneath (1961)


 STAND  Performs a linear transformation of a data matrix so as to eliminate the effects of different scales of measurement. Several options for
what gets subtracted off and what gets used as a divisor.

 SVD  Computes a singularvalue decomposition of a rectangular matrix. It allows you to compute principal axes and projections in a single step.

 TPSWTS  Computes projections of the 2D or 3D coordinates of objects onto the principal warps of a thinplate spline bending energy matrix (see Bookstein, 1991). This is done to enable a statistical analysis of the components of shape variation.

 TRANSF  Performs various linear and nonlinear transformations of the rows or columns of a matrix.
Computes Bookstein shape coordinates (both scaled and unscaled). Can also be used to delete rows or columns and alter the form of storage of some matrices.

 TREE  Displays a tree (e.g., from a cluster analysis) as a phenogram. Options are provided for scaling and scrolling through a tree interactively.
