Recently developed small-sample asymptotics provide nearly exact inference for parametric statistical models. One approach is via approximate conditional and marginal inference, respectively, in multiparameter exponential families and regression-scale models. Although the theory is well developed, these methods are under-used in practical work. This article presents a set of S-Plus routines for approximate conditional inference in logistic and loglinear regression models. It represents the first step of a project to create a library for small-sample inference which will include methods for some of the most widely used statistical models. Details of how the methods have been implemented are discussed. An example illustrates the code.

Approximate conditional inference in logistic and loglinear models / Brazzale, Alessandra Rosalba. - In: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS. - ISSN 1061-8600. - STAMPA. - 8(1999), pp. 653-661.

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