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' ...

Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods.

User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that ...

A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported.

The C++ header files of the Stan project are provided by this package, but it contains no R code or function documentation. There is a shared object containing part of the 'CVODES' library, but it is not accessible from R.

R interface to the 'Spectra' library <https://spectralib.org/> for large-scale eigenvalue and SVD problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more ...

Provides a general framework for high-dimensional undirected graph estimation. It integrates data preprocessing, neighborhood screening, graph estimation, and model selection techniques into a pipeline.

Previously an R wrapper of the 'ARPACK' library <http://www.caam.rice.edu/software/ARPACK/>, and now a shell of the R package 'RSpectra', an R interface to the 'Spectra' library <http://yixuan.cos.name/spectra/> for solving large scale ...

Simulate, estimate and forecast using univariate and multivariate GAS models as described in Ardia et al. (2019) <doi:10.18637/jss.v088.i06>.

A suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression.

Interface of 'MIXMOD' software for supervised, unsupervised and semi-supervised classification with mixture modelling.