center | % If @var{x} is a vector, subtract its mean. |

cloglog | % Return the complementary log-log function of @var{x}, defined as |

cor | % Compute correlation. |

corrcoef | % Compute correlation. |

cov | % Compute covariance. |

cut | % Create categorical data out of numerical or continuous data by |

gls | % Generalized least squares estimation for the multivariate model |

histc | % Produce histogram counts. |

iqr | % If @var{x} is a vector, return the interquartile range, i.e., the |

kendall | % Compute Kendall's @var{tau} for each of the variables specified by |

kurtosis | % If @var{x} is a vector of length @math{N}, return the kurtosis |

logit | % For each component of @var{p}, return the logit of @var{p} defined as |

mahalanobis | % Return the Mahalanobis' D-square distance between the multivariate |

mean | % If @var{x} is a vector, compute the mean of the elements of @var{x} |

meansq | % For vector arguments, return the mean square of the values. |

median | % If @var{x} is a vector, compute the median value of the elements of |

mode | % Count the most frequently appearing value. @code{mode} counts the |

moment | % If @var{x} is a vector, compute the @var{p}-th moment of @var{x}. |

ols | % Ordinary least squares estimation for the multivariate model |

ppplot | % Perform a PP-plot (probability plot). |

prctile | % For a sample @var{x}, compute the quantiles, @var{y}, corresponding |

probit | % For each component of @var{p}, return the probit (the quantile of the |

qqplot | % Perform a QQ-plot (quantile plot). |

quantile | % For a sample, @var{x}, calculate the quantiles, @var{q}, corresponding to |

range | % If @var{x} is a vector, return the range, i.e., the difference |

ranks | % Return the ranks of @var{x} along the first non-singleton dimension |

run_count | % Count the upward runs along the first non-singleton dimension of |

skewness | % If @var{x} is a vector of length @math{n}, return the skewness |

spearman | % Compute Spearman's rank correlation coefficient @var{rho} for each of |

statistics | % If @var{x} is a matrix, return a matrix with the minimum, first |

std | % If @var{x} is a vector, compute the standard deviation of the elements |

studentize | % If @var{x} is a vector, subtract its mean and divide by its standard |

table | % Create a contingency table @var{t} from data vectors. The @var{l} |

values | % Return the different values in a column vector, arranged in ascending |

var | % For vector arguments, return the (real) variance of the values. |