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Linear mixed models and penalized least squares
Linear mixed-effects models are an important class of statistical models that are used directly in many fields of applications and also are used as iterative steps in fitting other types ofExpand
Genomic representations using concatenates of Type IIB restriction endonuclease digestion fragments.
TLDR
It is shown that the RECORD libraries may be used for digital karyotyping and for pathogen identification by computational subtraction in genomic representation using Type IIB restriction endonucleases. Expand
Computational Methods for Single Level Linear Mixed-effects Models
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 ofExpand
Editorial New Features in Version 1.3.0 New Features in Version 1.2.3 New Features in Version 1.2.2 Changes on Cran Genesom Clustering Genes Using Self-organizing Rmysql Database Interface and Mysql
Welcome to the second issue of R News, the newsletter of the R project for statistical computing. First we would like to thank for all the positive feedback we got on the first volume, whichExpand
C++ Classes for R Objects
When creating the R Matrix package, which provides access to the Fortran Lapack and BLAS3 routines, we patterned the functions after the corresponding S-PLUS library but chose a completely dierentExpand
Computational Methods for Multiple Level Linear Mixed-effects Models
In an earlier paper we provided easily-calculated expressions for the gradient of the profiled log-likelihood and log-restricted-likelihood for single-level mixed-effects models. We also showed howExpand
Converting a large R package to S4 classes and methods
TLDR
This project reimplemented linear mixed-eects (lme) models using S4 classes and methods using John Chambers’ book“Programming with Data” and as implemented in the methods package for R to provide a faster, cleaner implementation of lme fits in R while including hooks for later extensions to the glmm model and the nlme model. Expand
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