g02aa computes the nearest correlation matrix, in the Frobenius norm, to a given square, input matrix.

g02ab computes the nearest correlation matrix, in the Frobenius norm or weighted Frobenius norm, and optionally with bounds on the eigenvalues, to a given square, input matrix.

g02ae computes the factor loading matrix associated with the nearest correlation matrix with $k$-factor structure, in the Frobenius norm, to a given square, input matrix.

g02ba computes means and standard deviations of variables, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for a set of data.

g02bb computes means and standard deviations of variables, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for a set of data omitting completely any cases with a missing observation for any variable.

g02bc computes means and standard deviations of variables, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for a set of data omitting cases with missing values from only those calculations involving the variables for which the values are missing.

g02bd computes means and standard deviations of variables, sums of squares and cross-products about zero, and correlation-like coefficients for a set of data.

g02be computes means and standard deviations of variables, sums of squares and cross-products about zero, and correlation-like coefficients for a set of data omitting completely any cases with a missing observation for any variable.

g02bf computes means and standard deviations of variables, sums of squares and cross-products about zero and correlation-like coefficients for a set of data omitting cases with missing values from only those calculations involving the variables for which the values are missing.

g02bg computes means and standard deviations, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for selected variables.

g02bh computes means and standard deviations, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for selected variables omitting completely any cases with a missing observation for any variable (either over all variables in the dataset or over only those variables in the selected subset).

g02bj computes means and standard deviations, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for selected variables omitting cases with missing values from only those calculations involving the variables for which the values are missing.

g02bk computes means and standard deviations, sums of squares and cross-products about zero, and correlation-like coefficients for selected variables.

g02bl computes means and standard deviations, sums of squares and cross-products about zero, and correlation-like coefficients for selected variables omitting completely any cases with a missing observation for any variable (either over all variables in the dataset or over only those variables in the selected subset).

g02bm computes means and standard deviations, sums of squares and cross-products about zero, and correlation-like coefficients for selected variables omitting cases with missing values from only those calculations involving the variables for which the values are missing.

g02bn computes Kendall and/or Spearman nonparametric rank correlation coefficients for a set of data; the data array is overwritten with the ranks of the observations.

g02bp computes Kendall and/or Spearman nonparametric rank correlation coefficients for a set of data omitting completely any cases with a missing observation for any variable; the data array is overwritten with the ranks of the observations.

g02bq computes Kendall and/or Spearman nonparametric rank correlation coefficients for a set of data; the data array is preserved, and the ranks of the observations are not available on exit from the method.

g02br computes Kendall and/or Spearman nonparametric rank correlation coefficients for a set of data, omitting completely any cases with a missing observation for any variable; the data array is preserved, and the ranks of the observations are not available on exit from the method.

g02bs computes Kendall and/or Spearman nonparametric rank correlation coefficients for a set of data omitting cases with missing values from only those calculations involving the variables for which the values are missing; the data array is preserved, and the ranks of the observations are not available on exit from the method.

g02bt updates the sample means and sums of squares and cross-products, or sums of squares and cross-products of deviations about the mean, for a new observation. The data may be weighted.

g02bu calculates the sample means and sums of squares and cross-products, or sums of squares and cross-products of deviations from the mean, in a single pass for a set of data. The data may be weighted.

g02bw calculates a matrix of Pearson product-moment correlation coefficients from sums of squares and cross-products of deviations about the mean.

g02bx calculates the sample means, the standard deviations, the variance-covariance matrix, and the matrix of Pearson product-moment correlation coefficients for a set of data. Weights may be used.

g02by computes a partial correlation/variance-covariance matrix from a correlation or variance-covariance matrix computed by g02bx.

g02ca performs a simple linear regression with dependent variable $y$ and independent variable $x$.

g02cb performs a simple linear regression with no constant, with dependent variable $y$ and independent variable $x$.

g02cc performs a simple linear regression with dependent variable $y$ and independent variable $x$, omitting cases involving missing values.

g02cd performs a simple linear regression with no constant, with dependent variable $y$ and independent variable $x$, omitting cases involving missing values.

g02ce takes selected elements from two vectors (typically vectors of means and standard deviations) to form two smaller vectors, and selected rows and columns from two matrices (typically either matrices of sums of squares and cross-products of deviations from means and Pearson product-moment correlation coefficients, or matrices of sums of squares and cross-products about zero and correlation-like coefficients) to form two smaller matrices, allowing reordering of elements in the process.

g02cf reorders the elements in two vectors (typically vectors of means and standard deviations), and the rows and columns in two matrices (typically either matrices of sums of squares and cross-products of deviations from means and Pearson product-moment correlation coefficients, or matrices of sums of squares and cross-products about zero and correlation-like coefficients).

g02cg performs a multiple linear regression on a set of variables whose means, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients are given.

g02ch performs a multiple linear regression with no constant on a set of variables whose sums of squares and cross-products about zero and correlation-like coefficients are given.

g02da performs a general multiple linear regression when the independent variables may be linearly dependent. Parameter estimates, standard errors, residuals and influence statistics are computed. g02da may be used to perform a weighted regression.

g02dc adds or deletes an observation from a general regression model fitted by g02da.

g02dd calculates the regression parameters for a general linear regression model. It is intended to be called after g02dc, g02de or g02df.

g02de adds a new independent variable to a general linear regression model.

g02df deletes an independent variable from a general linear regression model.

g02dg calculates the estimates of the parameters of a general linear regression model for a new dependent variable after a call to g02da.

g02dk calculates the estimates of the parameters of a general linear regression model for given constraints from the singular value decomposition results.

g02dn gives the estimate of an estimable function along with its standard error.

g02ef calculates a full stepwise selection from $p$ variables by using Clarke's sweep algorithm on the correlation matrix of a design and data matrix, $Z$. The (weighted) variance-covariance, (weighted) means and sum of weights of $Z$ must be supplied.

g02fa calculates two types of standardized residuals and two measures of influence for a linear regression.

g02fc calculates the Durbin–Watson statistic, for a set of residuals, and the upper and lower bounds for its significance.

g02ga fits a generalized linear model with normal errors.

g02gb fits a generalized linear model with binomial errors.

g02gc fits a generalized linear model with Poisson errors.

g02gd fits a generalized linear model with gamma errors.

g02gk calculates the estimates of the parameters of a generalized linear model for given constraints from the singular value decomposition results.

g02gn gives the estimate of an estimable function along with its standard error from the results from fitting a generalized linear model.

g02gp allows prediction from a generalized linear model fit via g02ga, g02gb, g02gc or g02gd.

g02ha performs bounded influence regression ($M$-estimates). Several standard methods are available.

g02hb finds, for a real matrix $X$ of full column rank, a lower triangular matrix $A$ such that ${\left(A^{\mathrm{T}}A\right)}^{-1}$ is proportional to a robust estimate of the covariance of the variables. g02hb is intended for the calculation of weights of bounded influence regression using g02hd.

g02hd performs bounded influence regression ($M$-estimates) using an iterative weighted least squares algorithm.

Auxiliary for use as a delegate parameter

g02hf calculates an estimate of the asymptotic variance-covariance matrix for the bounded influence regression estimates (M-estimates). It is intended for use with g02hd.

g02hk computes a robust estimate of the covariance matrix for an expected fraction of gross errors.

g02hl calculates a robust estimate of the covariance matrix for user-supplied weight functions and their derivatives.

g02hm computes a robust estimate of the covariance matrix for user-supplied weight functions. The derivatives of the weight functions are not required.

g02ja fits a linear mixed effects regression model using restricted maximum likelihood (REML).

g02jb fits a linear mixed effects regression model using maximum likelihood (ML).

g02ka calculates a ridge regression, optimizing the ridge parameter according to one of four prediction error criteria.

g02kb calculates a ridge regression, with ridge parameters supplied by you.

g02la fits an orthogonal scores partial least squares (PLS) regression by using singular value decomposition.

g02lb fits an orthogonal scores partial least squares (PLS) regression by using Wold's iterative method.

g02lc calculates parameter estimates for a given number of factors given the output from an orthogonal scores PLS regression (g02la or g02lb).

g02ld calculates predictions given the output from an orthogonal scores PLS regression (g02la or g02lb) and g02lc.

g02qf performs a multiple linear quantile regression, returning the parameter estimates and associated confidence limits based on an assumption of Normal, independent, identically distributed errors. g02qf is a simplified version of g02qg.

g02qg performs a multiple linear quantile regression. Parameter estimates and, if required, confidence limits, covariance matrices and residuals are calculated. g02qg may be used to perform a weighted quantile regression. A simplified interface for g02qg is provided by g02qf.