Douglas M. Bates

Mixed-Effects Models in Sand S-PLUS

Bioconductor: open software development for computational biology and bioinformatics

Mixed-effects modeling with crossed random effects for subjects and items

Mixed-Effects Models in S and S-PLUS

Mixed-effects models provide a flexible and powerful tool for the analysis of grouped data, which arise in many areas as diverse as agriculture, biology, economics, manufacturin...

Fitting Linear Mixed-Effects Models Using<b>lme4</b>

Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 packa...

Estimating the Multilevel Rasch Model: With the<b>lme4</b>Package

Traditional Rasch estimation of the item and student parameters via marginal maximum likelihood, joint maximum likelihood or conditional maximum likelihood, assume individuals i...

The Computation of Generalized Cross-Validation Functions Through Householder Tridiagonalization with Applications to the Fitting of Interaction Spline Models

An efficient algorithm for computing the GCV (generalized cross-validation) function for the general cross-validated regularization/smoothing problem is provided. This algorithm...