Classical modeling techniques which assume independence of the observations are not appropriate for grouped data. Some features of NLME 3.3 Maximum likelihood and restricted maximum likelihood estimation Multilevel models for data with multiple nested grouping levels. These incorporate novel computational methods, described in the Bell Labs Technical Memorandum Computational Methods for Multilevel Models , available available here as PostScript or as Portable Document Format . Methods for plotting, summarizing, and fitting groupedData objects are also included. The plot methods for fitted objects include a formula argument which gives them unlimited flexibility for generating diagnostic plots.
Users can also define their own classes of positive-definite matrices.
Users can extend the NLME library by writing their own classes of correlation structures. Because the correlation structure and the variance function are independently specified, heterogeneous AR1, ARMA, etc. User-defined variance functions can be used to extend the NLME library. You can think of it as lme without random effects, or lm with correlation structures and variance functions. You can think of it as nlme without random effects, or nls with correlation structures and variance functions.