# Douglas Bates

#### 25 packages on CRAN

Datasets from Nelson, Coffin and Copeland "Introductory Statistics for Engineering Experimentation" (Elsevier, 2003) with sample code.

Provides functions to evaluate common pharmacokinetic/pharmacodynamic models and their gradients.

Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, 2019.

Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain.

Data sets and sample analyses from Jay L. Devore (2008), "Probability and Statistics for Engineering and the Sciences (7th ed)", Thomson.

Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner (2011) <doi:10.32614/RJ-2011-012> to help safeguard against convergence issues.

Functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others. Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc.

Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).

A rich hierarchy of matrix classes, including triangular, symmetric, and diagonal matrices, both dense and sparse and with pattern, logical and numeric entries. Numerous methods for and operations on these matrices, using 'LAPACK' and 'SuiteSparse' libraries.

Modelling with sparse and dense 'Matrix' matrices, using modular prediction and response module classes.

Data sets and sample analyses from Pinheiro and Bates, "Mixed-effects Models in S and S-PLUS" (Springer, 2000).

Derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell.

Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature.

Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003) <doi:10.1093/aob/mcg029>. There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Twenty one (21) new self-start (SS) functions in total. This package also supports the publication 'Nonlinear regression Models and applications in agricultural research' by Archontoulis and Miguez (2015) <doi:10.2134/agronj2012.0506>, a book chapter with similar material <doi:10.2134/appliedstatistics.2016.0003.c15> and a publication by Oddi et. al. (2019) in Ecology and Evolution <doi:10.1002/ece3.5543>. The function 'nlsLMList' uses nlsLM for fitting, but it is otherwise almost identical to 'nlme::nlsList'.In addition, this release of the package provides functions for conducting simulation for 'nlme' and 'gnls' objects as well as bootstrapping. These functions are intended to work with the modeling framework of the 'nlme' package. It also provides four vignettes with extended examples.

Tools to sort, edit and prune pedigrees and to extract the inbreeding coefficients and the relationship matrix (includes code for pedigrees from self-pollinated species). The use of pedigree data is central to genetics research within the animal and plant breeding communities to predict breeding values. The relationship matrix between the individuals can be derived from pedigree structure following the algorithms described for example in Vazquez et al., 2010 <doi:10.2527/jas.2009-1952>.

The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013, <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018, <doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details.

'Blaze' is an open-source, high-performance C++ math library for dense and sparse arithmetic. With its state-of-the-art Smart Expression Template implementation 'Blaze' combines the elegance and ease of use of a domain-specific language with 'HPC'-grade performance, making it one of the most intuitive and fastest C++ math libraries available. The 'Blaze' library offers: - high performance through the integration of 'BLAS' libraries and manually tuned 'HPC' math kernels - vectorization by 'SSE', 'SSE2', 'SSE3', 'SSSE3', 'SSE4', 'AVX', 'AVX2', 'AVX-512', 'FMA', and 'SVML' - parallel execution by 'OpenMP', C++11 threads and 'Boost' threads ('Boost' threads are disabled in 'RcppBlaze') - the intuitive and easy to use API of a domain specific language - unified arithmetic with dense and sparse vectors and matrices - thoroughly tested matrix and vector arithmetic - completely portable, high quality C++ source code. The 'RcppBlaze' package includes the header files from the 'Blaze' library with disabling some functionalities related to link to the thread and system libraries which make 'RcppBlaze' be a header-only library. Therefore, users do not need to install 'Blaze' and the dependency 'Boost'. 'Blaze' is licensed under the New (Revised) BSD license, while 'RcppBlaze' (the 'Rcpp' bindings/bridge to 'Blaze') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Note that since 'Blaze' has committed to 'C++14' commit to 'C++14' which does not used by most R users from version 3.0, we will use the version 2.6 of 'Blaze' which is 'C++98' compatible to support the most compilers and system.

R and 'Eigen' integration using 'Rcpp'. 'Eigen' is a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms. It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems. Its performance on many algorithms is comparable with some of the best implementations based on 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header files from the 'Eigen' C++ template library (currently version 3.3.4). Thus users do not need to install 'Eigen' itself in order to use 'RcppEigen'. Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License (version 2); earlier version were licensed under the GNU LGPL version 3 or later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.

Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

Data sets and sample lmer analyses corresponding to the examples in Littell, Milliken, Stroup and Wolfinger (1996), "SAS System for Mixed Models", SAS Institute.

Formula-based user-interfaces to specific transformation models implemented in package 'mlt'. Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to transformation models for clustered data is provided (Hothorn, 2019, <arxiv:1910.09219>).