Through the benefits of mixed modeling over the usual fixed effects modeling, certain data sets can be better understood. Repeated measures data is especially well suited to mixed models. The repeated measures can be used as random effects and thus improve the modeling process through the mixed model. Statistical software such as R is now becoming increasingly more useful in using these mixed models. Through using R, repeated measures data is readily modeled using generalised linear mixed models. There are two packages in R which can perform generalised linear mixed models. The application of both these packages in modeling generalised linear mixed models is explained simply and concisely, with straightforward guidelines given to assist in the modeling process. Comparisons are made between the modeling methods within each of the two packages, with both the benefits and limitations of each package highlighted. Methods for additional ana.lysis on these models are also described. Repeated measures count data on New Zealand birds is used as an example to show exactly how to implement generalised linear mixed models in both packages in R.