Generalized linear modeling (GLM) is currently undergoing a renaissance. The number of software packages offering GLM capability grows each year and as a partial consequence one finds an increased number of research endeavors being modeled using GLM methodology. On the other hand, there have likewise been an increasing number of requests to vendors by users of statistical packages to include GLM facilities amid other offerings. The overall effect has been a near 300 percent increase in GLM programs over the past four years.

I shall discuss the nature of generalized linear models followed by an examination of how they have been implemented by statistical software companies or enterprises. I shall thereupon advocate a different pedagogical approach to structuring GLM programs than has generally been practiced and shall discuss the inclusion of the negative binomial into the standard GLM package offerings. A negative binomial algorithm, including the canonical and power link forms, will be presented in a manner that can easily be incorporated into current GLM packages. (c) Joseph Hilbe, 1993