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The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of(More)
Nonlinear mixed effects models have received a great deal of attention in the statistical literature in recent years because of the flexibility they offer in handling unbalanced repeated measures data that arise in different areas of investigation, such as pharmacokinetics and economics. Several different methods for estimating the parameters in nonlinear(More)
The estimation of variance-covariance matrices in situations that involve the optimization of an objective function (e.g. a log-likelihood function) is usually a difficult numerical problem, since the resulting estimates should be positive semi-definite matrices. We can either use constrained optimization , or employ a parameterization that enforces this(More)
Mixed-effects models are frequently used to analyze grouped data, because they flexibly model the within-group correlation often present in this type of data. Examples of grouped data include longitudinal data, repeated measures data, multilevel data, and split-plot designs. We describe a set of S functions, classes, and methods for the analysis of linear(More)
lme and nlme: Mixed-effects Methods and Classes for S and S-plus Mixed-effects models provide a powerful and flexible tool for analyzing clustered data, such as repeated measures data and nested designs. We describe a set of S functions, classes, and methods for the analysis of both linear and nonlinear mixed-effects models. These extend the linear and(More)