The `lme4` package, version 1.1-36, is designed for fitting and analyzing linear mixed-effects models using the 'Eigen' C++ library and 'RcppEigen' for numerical linear algebra. It supports both linear and generalized linear mixed-effects models, represented using S4 classes and methods. The package depends on R (>= 3.6.0), Matrix, methods, and stats, and links to Rcpp, RcppEigen, and Matrix. It imports several other packages and suggests additional packages for advanced functionalities. Key features include: - Efficient linear algebra methods using 'Eigen'. - Reference classes to avoid unnecessary copying of large objects. - Modular internal structure for better control over different steps of argument checking and model construction. - Profiling and parametric bootstrapping. - Support for various optimization methods through 'RcppEigen' and 'optimx', 'nloptim', and 'dfoptim'. The package also includes functions for model diagnostics, confidence intervals, and bootstrap methods. It provides detailed documentation and examples for various functionalities, such as fitting models, extracting components, and performing statistical tests. The package is maintained by Ben Bolker and has contributions from several other authors.The `lme4` package, version 1.1-36, is designed for fitting and analyzing linear mixed-effects models using the 'Eigen' C++ library and 'RcppEigen' for numerical linear algebra. It supports both linear and generalized linear mixed-effects models, represented using S4 classes and methods. The package depends on R (>= 3.6.0), Matrix, methods, and stats, and links to Rcpp, RcppEigen, and Matrix. It imports several other packages and suggests additional packages for advanced functionalities. Key features include: - Efficient linear algebra methods using 'Eigen'. - Reference classes to avoid unnecessary copying of large objects. - Modular internal structure for better control over different steps of argument checking and model construction. - Profiling and parametric bootstrapping. - Support for various optimization methods through 'RcppEigen' and 'optimx', 'nloptim', and 'dfoptim'. The package also includes functions for model diagnostics, confidence intervals, and bootstrap methods. It provides detailed documentation and examples for various functionalities, such as fitting models, extracting components, and performing statistical tests. The package is maintained by Ben Bolker and has contributions from several other authors.