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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)
Linear mixed-effects models are an important class of statistical models that are not only used directly in many fields of applications but also used as iterative steps in fitting other types of mixed-effects models, such as generalized linear mixed models. The parameters in these models are typically estimated by maximum likelihood (ML) or restricted(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)
Key Words and Phrases: ill-posed problems; partial thin plate smoothing splines; penalized likelihood ; semi-parametric models; ridge regression; thin plate smoothing splines; truncated singular value decomposition. Purpose These Fortran-77 subroutines provide building blocks for Generalized Cross-Validation (GCV) (Craven and Wahba, 1979) calculations in(More)
A multilevel mixed-effects model has random effects at each of several nested levels of grouping of the observed responses. We may use these, for example, when modelling observations taken over time on students who are grouped into classes that are grouped into schools that are grouped into districts. If each of the distributions of the random effects is(More)
Linear mixed-effects models are an important class of statistical models that are used directly in many fields of applications and are also used as iterative steps in fitting other types of mixed-effects models, such as generalized linear mixed models. The parameters in these models are typically estimated by maximum likelihood (ML) or restricted maximum(More)