This book provides an overview of the theory and application of linearand nonlinear mixed-effects models in the analysis of grouped data,such as longitudinal data, repeated measures, and multilevel data. Aunified model-building strategy for both linear and nonlinear modelsis presented and applied to the analysis of over 20 real datasets froma wide variety of areas, including pharmacokinetics, agriculture, andmanufacturing. A strong emphasis is placed on the use of graphicaldisplays at the various phases of the model-building process, startingwith exploratory plots of the data and concluding with diagnosticplots to assess the adequacy of a fitted model. Over 170 figures areincluded in the book.The NLME library for analyzing mixed-effects models in S andS-PLUS, developed by the authors, provides the underlyingsoftware for implementing the methods presented in the text, beingdescribed and illustrated in detail throughout the book.The balanced mix of real data examples, modeling software, and theorymakes this book a useful reference for practitioners using mixed-effects models in their data analyses. It can also be used as a text for a one-semester graduate-level applied course in mixed-effects models. Researchers in statistical computing will also find this book appealing for its presentation of novel andefficient computational methods for fitting linear and nonlinearmixed-effects models.Jose C. Pinheiro has been a member of the technical staff instatistics research at Bell Laboratories since 1996. He received hisPh.D. in Statistics from and worked for two years in the Department of Biostatistics at the University of Wisconsin-Madison. The author of several articles in mixed-effects models, he is a member of the American Statistical Association and the Biometric Society. Douglas M. Bates is Professor of Statistics at the University of Wisconsin-Madison. He is the author, with Donald G. Watts, of Nonlinear Regression Analysis and Its Applications, aFellow of the American Statistical Association, and a former chair ofits Statistical Computing Section.