'C++' Headers for 'ggdmc' Package

A fast 'C++' implementation of the design-based, Diffusion Decision Model (DDM) and the Linear Ballistic Accumulation (LBA) model. It enables the user to optimise the choice response time model by connecting with the Differential Evolution Markov Chain Monte Carlo (DE-MCMC) sampler implemented in the 'ggdmc' package. The package fuses the hierarchical modelling, Bayesian inference, choice response time models and factorial designs, allowing users to build their own design-based models. For more information on the underlying models, see the works by Voss, Rothermund, and Voss (2004) , Ratcliff and McKoon (2008) , and Brown and Heathcote (2008) .

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'.

C-Statistics for Risk Prediction Models with Censored Survival Data

Performs inference for C of risk prediction models with censored survival data, using the method proposed by Uno et al. (2011) . Inference for the difference in C between two competing prediction models is also implemented.

Functions for Timing R Scripts, as Well as Implementations of "Stack" and "StackList" Structures

Code execution timing functions 'tic' and 'toc' that can be nested. One can record all timings while a complex script is running, and examine the values later. It is also possible to instrument the timing calls with custom callbacks. In addition, this package provides class 'Stack', implemented as a vector, and class 'StackList', which is a stack implemented as a list, both of which support operations 'push', 'pop', 'first_element', 'last_element' and 'clear'.

Diffs for R Objects

Generate a colorized diff of two R objects for an intuitive visualization of their differences.

Fast Fixed-Effects Estimations

Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), instrumental variables (IV), generalized linear models (GLM), maximum likelihood estimation (ML), and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Bergé, Butts, McDermott (2026) . Further provides tools to export and view the results of several estimations with intuitive design to change the standard-errors.

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.

Ensemble Empirical Mode Decomposition (EEMD) and Its Complete Variant (CEEMDAN)

An R interface for libeemd (Luukko, Helske, Räsänen, 2016) , a C library of highly efficient parallelizable functions for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). Due to the possible portability issues CRAN version no longer supports OpenMP, but you can install OpenMP-supported version from GitHub: < https://github.com/helske/Rlibeemd/>.

Functions for Nonlinear Least Squares Solutions - Updated 2022

Provides tools for working with nonlinear least squares problems. For the estimation of models reliable and robust tools than nls(), where the the Gauss-Newton method frequently stops with 'singular gradient' messages. This is accomplished by using, where possible, analytic derivatives to compute the matrix of derivatives and a stabilization of the solution of the estimation equations. Tools for approximate or externally supplied derivative matrices are included. Bounds and masks on parameters are handled properly.