Abstract
Computer packages for nonlinear modeling may be classified as follows: (1) Customized routines with local conventions, for particular curves, distributions or other models, such as the logistic curve, the Weibull distribution, or the probit line for quantal response in bioassay. (2) General optimization algorithms supplied by subroutine libraries: users must organize their data, output, initial values, and constants for the optimization algorithm. (3) General statistical algorithms such as iteratively weighted least squares, which handle a subclass of nonlinear models in a standard manner, such as GLIM. (4) General statistical packages which include options for general model fitting and optimization, such as GENSTAT, SAS, or BMDP. (5) Specialized nonlinear modeling packages with facilities for both standard models and general user-defined models, such as the maximum likelihood program MLP (Chapter 7).
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