regression

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 routines for multiple linear regression, select elements from vectors and matrices
G02CFF   Service routines 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 variable to a general linear regression model
G02DFF   Delete a variable from a general linear regression model
G02DGF   Fits a general linear regression model for 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
G02EEF   Fits a linear regression model by forward selection
G02HAF   Robust regression, standard M-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
G08RAF   Regression using ranks, uncensored data
G08RBF   Regression using ranks, right-censored data

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© The Numerical Algorithms Group Ltd, Oxford UK. 2001