This research will develop a statistical software library in S-PLUS for dropout data. Missing and dropout data are common nature in longitudinal studies. When the dropout process is related to the outcome process, it creates tremendous challenges in analyzing such data. No commercial software currently considers the dropout mechanisms in dealing with informative or non-random dropout. Consequently, the results are biased and misleading. The ultimate objective of this research is the development of a statistical software library for analyzing dropout data using both pattern mixture and selection model approaches. The approaches apply linear models, generalized linear mixed-effects models or GEE models for the response process and a regression using a Iogit, a probit or a Clog-log link for the dropout process. This library will include methods for parameter estimation, sensitivity analysis, graphical analysis, and model selection. The algorithms developing for parameter estimation include stochastic EM, likelihood maximization and imputation methods. Graphical tools will be developed for displaying dropout data, monitoring parameter convergence and diagnosing fitted values. Sensitivity analysis based on analytic and graphic methods are useful on testing the validity of the modeling assumptions. Comprehensive case studies and simulations will show the advantage and the applicability of the results of this investigation. [unreadable] [unreadable]