R Interface to NLopt

Solve optimization problems using an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. See < https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/> for more information on the available algorithms. Building from included sources requires 'CMake'. On Linux and 'macOS', if a suitable system build of NLopt (2.7.0 or later) is found, it is used; otherwise, it is built from included sources via 'CMake'. On Windows, NLopt is obtained through 'rwinlib' for 'R <= 4.1.x' or grabbed from the appropriate toolchain for 'R >= 4.2.0'.

'Rcpp' Integration of Additional Probability Distributions

The 'Rcpp' package provides a C++ library to make it easier to use C++ with R. R and 'Rcpp' provide functions for a variety of statistical distributions. Several R packages make functions available to R for additional statistical distributions. However, to access these functions from C++ code, a costly call to the R functions must be made. 'RcppDist' provides a header-only C++ library with functions for additional statistical distributions that can be called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'. Functions are available that return a 'NumericVector' as well as doubles, and for multivariate or matrix distributions, 'Armadillo' vectors and matrices. 'RcppDist' provides functions for the following distributions: the four parameter beta distribution; the location- scale t distribution; the truncated normal distribution; the truncated t distribution; a truncated location-scale t distribution; the triangle distribution; the multivariate normal distribution*; the multivariate t distribution*; the Wishart distribution*; and the inverse Wishart distribution*. Distributions marked with an asterisk rely on 'RcppArmadillo'.

Tools for Developing R Packages Interfacing with 'Stan'

Provides various tools for developers of R packages interfacing with 'Stan' < https://mc-stan.org>, including functions to set up the required package structure, S3 generics and default methods to unify function naming across 'Stan'-based R packages, and vignettes with recommendations for developers.

Parallel Distance Matrix Computation using Multiple Threads

A fast parallelized alternative to R's native 'dist' function to calculate distance matrices for continuous, binary, and multi-dimensional input matrices, which supports a broad variety of 41 predefined distance functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user- defined functions written in C++. For ease of use, the 'parDist' function extends the signature of the 'dist' function and uses the same parameter naming conventions as distance methods of existing R packages. The package is mainly implemented in C++ and leverages the 'RcppParallel' package to parallelize the distance computations with the help of the 'TinyThread' library. Furthermore, the 'Armadillo' linear algebra library is used for optimized matrix operations during distance calculations. The curiously recurring template pattern (CRTP) technique is applied to avoid virtual functions, which improves the Dynamic Time Warping calculations while the implementation stays flexible enough to support different DTW step patterns and normalization methods.

Extreme Gradient Boosting

Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) . This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.

Topic Models

Provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.

Additional Univariate and Multivariate Distributions

Density, distribution function, quantile function and random generation for a number of univariate and multivariate distributions. This package implements the following distributions: Bernoulli, beta-binomial, beta-negative binomial, beta prime, Bhattacharjee, Birnbaum-Saunders, bivariate normal, bivariate Poisson, categorical, Dirichlet, Dirichlet-multinomial, discrete gamma, discrete Laplace, discrete normal, discrete uniform, discrete Weibull, Frechet, gamma-Poisson, generalized extreme value, Gompertz, generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t, Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy, Laplace, location-scale t, logarithmic, Lomax, multivariate hypergeometric, multinomial, negative hypergeometric, non-standard beta, normal mixture, Poisson mixture, Pareto, power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam, slash, triangular, truncated binomial, truncated normal, truncated Poisson, Tukey lambda, Wald, zero-inflated binomial, zero-inflated negative binomial, zero-inflated Poisson.

R Interface to C API of GLPK

R Interface to C API of GLPK, depends on GLPK Version >= 4.42.

Rule- And Instance-Based Regression Modeling

Regression modeling using rules with added instance-based corrections.

Four-Step Biodiversity Analysis Based on 'iNEXT'

Expands 'iNEXT' to include the estimation of sample completeness and evenness. The package provides simple functions to perform the following four-step biodiversity analysis: STEP 1: Assessment of sample completeness profiles. STEP 2a: Analysis of size-based rarefaction and extrapolation sampling curves to determine whether the asymptotic diversity can be accurately estimated. STEP 2b: Comparison of the observed and the estimated asymptotic diversity profiles. STEP 3: Analysis of non-asymptotic coverage-based rarefaction and extrapolation sampling curves. STEP 4: Assessment of evenness profiles. The analyses in STEPs 2a, 2b and STEP 3 are mainly based on the previous 'iNEXT' package. Refer to the 'iNEXT' package for details. This package is mainly focusing on the computation for STEPs 1 and 4. See Chao et al. (2020) for statistical background.