Utilities from and Interface to the 'Bioinfo-C' ('BIOS') Library

Provides interface to the 'Bioinfo-C' (internal name: 'BIOS') library and utilities. 'ribiosUtils' is a Swiss-knife for computational biology in drug discovery, providing functions and utilities with minimal external dependency and maximal efficiency.

'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'.

The 'plog' C++ Logging Library

A simple header-only logging library for C++. Add 'LinkingTo: plogr' to 'DESCRIPTION', and '#include ' in your C++ modules to use it.

Space-Filling Random and Quasi-Random Sequences

Generates random and quasi-random space-filling sequences. Supports the following sequences: 'Halton', 'Sobol', 'Owen'-scrambled 'Sobol', 'Owen'-scrambled 'Sobol' with errors distributed as blue noise, progressive jittered, progressive multi-jittered ('PMJ'), 'PMJ' with blue noise, 'PMJ02', and 'PMJ02' with blue noise. Includes a 'C++' 'API'. Methods derived from "Constructing Sobol sequences with better two-dimensional projections" (2012) S. Joe and F. Y. Kuo, "Progressive Multi-Jittered Sample Sequences" (2018) < https://graphics.pixar.com/library/ProgressiveMultiJitteredSampling/paper.pdf> Christensen, P., Kensler, A. and Kilpatrick, C., and "A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space" (2019) E. Heitz, B. Laurent, O. Victor, C. David and I. Jean-Claude, .

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.

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.

The Uniform Manifold Approximation and Projection (UMAP) Method for Dimensionality Reduction

An implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018) . It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) is also provided. This is a complete re-implementation in R (and C++, via the 'Rcpp' package): no Python installation is required. See the uwot website (< https://github.com/jlmelville/uwot>) for more documentation and examples.

Advanced Graphics and Image-Processing in R

Bindings to 'ImageMagick': the most comprehensive open-source image processing library available. Supports many common formats (png, jpeg, tiff, pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio images are automatically previewed when printed to the console, resulting in an interactive editing environment. Also includes a graphics device for creating drawing onto images using pixel coordinates.

Spatial Dependence: Weighting Schemes, Statistics

A collection of functions to create spatial weights matrix objects from polygon 'contiguities', from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial 'autocorrelation', including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord' (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel' general cross product statistic, Empirical Bayes estimates and 'Assunção/Reis' (1999) Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992) and multicoloured join count statistics, 'APLE' ('Li et al.' ) , local 'Moran's I', 'Gearys C' ('Anselin' 1995) and 'Getis/Ord' G ('Ord' and 'Getis' 1995) , 'saddlepoint' approximations ('Tiefelsdorf' 2002) and exact tests for global and local 'Moran's I' ('Bivand et al.' 2009) and 'LOSH' local indicators of spatial heteroscedasticity ('Ord' and 'Getis') . The implementation of most of these measures is described in 'Bivand' and 'Wong' (2018) , with further extensions in 'Bivand' (2022) . 'Lagrange' multiplier tests for spatial dependence in linear models are provided ('Anselin et al'. 1996) , as are 'Rao' score tests for hypothesised spatial 'Durbin' models based on linear models ('Koley' and 'Bera' 2023) . Additions in 2024 include Local Indicators for Categorical Data based on 'Carrer et al.' (2021) and 'Bivand et al.' (2017) ; also Weighted Multivariate Spatial Autocorrelation Measures ('Bavaud' 2024) . . A local indicators for categorical data (LICD) implementation based on 'Carrer et al.' (2021) and 'Bivand et al.' (2017) was added in 1.3-7. Multivariate 'spatialdelta' ('Bavaud' 2024) was added in 1.3-13 ('Bivand' 2025 ). From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions previously present in this package are defunct in 'spdep' and may be found in 'spatialreg'.