| G02AAF
| Computes the nearest correlation matrix to a real square matrix, using the method of Qi and Sun |
| G02ABF
| Computes the nearest correlation matrix to a real square matrix, augmented G02AAF to incorporate weights and bounds |
| G02AEF
| Computes the nearest correlation matrix with -factor structure to a real square matrix |
| G02AJF
| Computes the nearest correlation matrix to a real square matrix, using element-wise weighting |
| G02BAF
| Pearson product-moment correlation coefficients, all variables, no missing values |
| G02BBF
| Pearson product-moment correlation coefficients, all variables, casewise treatment of missing values |
| G02BCF
| Pearson product-moment correlation coefficients, all variables, pairwise treatment of missing values |
| G02BDF
| Correlation-like coefficients (about zero), all variables, no missing values |
| G02BEF
| Correlation-like coefficients (about zero), all variables, casewise treatment of missing values |
| G02BFF
| Correlation-like coefficients (about zero), all variables, pairwise treatment of missing values |
| G02BGF
| Pearson product-moment correlation coefficients, subset of variables, no missing values |
| G02BHF
| Pearson product-moment correlation coefficients, subset of variables, casewise treatment of missing values |
| G02BJF
| Pearson product-moment correlation coefficients, subset of variables, pairwise treatment of missing values |
| G02BKF
| Correlation-like coefficients (about zero), subset of variables, no missing values |
| G02BLF
| Correlation-like coefficients (about zero), subset of variables, casewise treatment of missing values |
| G02BMF
| Correlation-like coefficients (about zero), subset of variables, pairwise treatment of missing values |
| G02BNF
| Kendall/Spearman non-parametric rank correlation coefficients, no missing values, overwriting input data |
| G02BPF
| Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, overwriting input data |
| G02BQF
| Kendall/Spearman non-parametric rank correlation coefficients, no missing values, preserving input data |
| G02BRF
| Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, preserving input data |
| G02BSF
| Kendall/Spearman non-parametric rank correlation coefficients, pairwise treatment of missing values |
| G02BTF
| Update a weighted sum of squares matrix with a new observation |
| G02BUF
| Computes a weighted sum of squares matrix |
| G02BWF
| Computes a correlation matrix from a sum of squares matrix |
| G02BXF
| Computes (optionally weighted) correlation and covariance matrices |
| G02BYF
| Computes partial correlation/variance-covariance matrix from correlation/variance-covariance matrix computed by G02BXF |
| G02BZF
| Combines two sums of squares matrices, for use after G02BUF |
| G02CAF
| Simple linear regression with constant term, no missing values |
| G02CBF
| Simple linear regression without constant term, no missing values |
| G02CCF
| Simple linear regression with constant term, missing values |
| G02CDF
| Simple linear regression without constant term, missing values |
| G02CEF
| Service routine for multiple linear regression, select elements from vectors and matrices |
| G02CFF
| Service routine for multiple linear regression, re-order elements of vectors and matrices |
| G02CGF
| Multiple linear regression, from correlation coefficients, with constant term |
| G02CHF
| Multiple linear regression, from correlation-like coefficients, without constant term |
| G02DAF
| Fits a general (multiple) linear regression model |
| G02DCF
| Add/delete an observation to/from a general linear regression model |
| G02DDF
| Estimates of linear parameters and general linear regression model from updated model |
| G02DEF
| Add a new independent variable to a general linear regression model |
| G02DFF
| Delete an independent variable from a general linear regression model |
| G02DGF
| Fits a general linear regression model to new dependent variable |
| G02DKF
| Estimates and standard errors of parameters of a general linear regression model for given constraints |
| G02DNF
| Computes estimable function of a general linear regression model and its standard error |
| G02EAF
| Computes residual sums of squares for all possible linear regressions for a set of independent variables |
| G02ECF
| Calculates and values from residual sums of squares |
| G02EEF
| Fits a linear regression model by forward selection |
| G02EFF
| Stepwise linear regression |
| G02FAF
| Calculates standardized residuals and influence statistics |
| G02FCF
| Computes Durbin–Watson test statistic |
| G02GAF
| Fits a generalized linear model with Normal errors |
| G02GBF
| Fits a generalized linear model with binomial errors |
| G02GCF
| Fits a generalized linear model with Poisson errors |
| G02GDF
| Fits a generalized linear model with gamma errors |
| G02GKF
| Estimates and standard errors of parameters of a general linear model for given constraints |
| G02GNF
| Computes estimable function of a generalized linear model and its standard error |
| G02GPF
| Computes a predicted value and its associated standard error based on a previously fitted generalized linear model |
| G02HAF
| Robust regression, standard -estimates |
| G02HBF
| Robust regression, compute weights for use with G02HDF |
| G02HDF
| Robust regression, compute regression with user-supplied functions and weights |
| G02HFF
| Robust regression, variance-covariance matrix following G02HDF |
| G02HKF
| Calculates a robust estimation of a correlation matrix, Huber's weight function |
| G02HLF
| Calculates a robust estimation of a correlation matrix, user-supplied weight function plus derivatives |
| G02HMF
| Calculates a robust estimation of a correlation matrix, user-supplied weight function |
| G02JAF
| Linear mixed effects regression using Restricted Maximum Likelihood (REML) |
| G02JBF
| Linear mixed effects regression using Maximum Likelihood (ML) |
| G02JCF
| Hierarchical mixed effects regression, initialization routine for G02JDF and G02JEF |
| G02JDF
| Hierarchical mixed effects regression using Restricted Maximum Likelihood (REML) |
| G02JEF
| Hierarchical mixed effects regression using Maximum Likelihood (ML) |
| G02KAF
| Ridge regression, optimizing a ridge regression parameter |
| G02KBF
| Ridge regression using a number of supplied ridge regression parameters |
| G02LAF
| Partial least squares (PLS) regression using singular value decomposition |
| G02LBF
| Partial least squares (PLS) regression using Wold's iterative method |
| G02LCF
| PLS parameter estimates following partial least squares regression by G02LAF or G02LBF |
| G02LDF
| PLS predictions based on parameter estimates from G02LCF |
| G02QFF
| Linear quantile regression, simple interface, independent, identically distributed (IID) errors |
| G02QGF
| Linear quantile regression, comprehensive interface |
| G02ZKF
| Option setting routine for G02QGF |
| G02ZLF
| Option getting routine for G02QGF |