R Interface for the RAPIDS cuML Suite of Libraries

R interface for RAPIDS cuML (< https://github.com/NVIDIA/cuml>), a suite of GPU-accelerated machine learning libraries powered by CUDA (< https://en.wikipedia.org/wiki/CUDA>).

R Interface for the RAPIDS cuML Suite of Libraries

The purpose of 'cuml4r' is to provide a simple and intuitive R interface for cuML (< https://github.com/rapidsai/cuml>). CuML is a suite of GPU-accelerated machine learning libraries powered by CUDA (< https://en.wikipedia.org/wiki/CUDA>).

'ViennaCL' C++ Header Files

'ViennaCL' is a free open-source linear algebra library for computations on many-core architectures (GPUs, MIC) and multi-core CPUs. The library is written in C++ and supports 'CUDA', 'OpenCL', and 'OpenMP' (including switches at runtime). I have placed these libraries in this package as a more efficient distribution system for CRAN. The idea is that you can write a package that depends on the 'ViennaCL' library and yet you do not need to distribute a copy of this code with your package.

Browser-Native 'WebGL' Rendering for R Graphics

Provides browser-native 'WebGL' rendering for R graphics through 'htmlwidgets'. The package supports grammar-style graphics workflows and renderer-ready specifications for dense analytical and scientific scenes, including point, line, trajectory, raster, vector, mesh, and surface layers, shader-driven display modes, timeline controls, structured views, selection metadata, and publication-oriented static export helpers. Rendering stays in the browser, and the core package remains cross-platform without requiring 'CUDA', 'Metal', or 'OpenCL' toolchains.

Design Clinical Trials with Potential Biomarker Effect

Applying 'CUDA' 'GPUs' via 'Numba' for optimal clinical design. It allows the user to utilize a 'reticulate' 'Python' environment and run intensive Monte Carlo simulation to get the optimal cutoff for the clinical design with potential biomarker effect, which can guide the realistic clinical trials.

Contemporaneous Markov Chain Monte Carlo

Implements contemporaneous Markov chain Monte Carlo (CMCMC) and interchain adaptive Markov chain Monte Carlo (INCA) samplers of Craiu, Rosenthal and Yang (2009) for targets known up to a normalising constant. The samplers run multiple Metropolis chains in parallel and update proposal covariance estimates using contemporaneous particle groups. Built-in target kernels include multivariate normal, logistic regression, Poisson, Gaussian, Gamma, and hierarchical models, with support for user-provided target kernels. The formula interface glm_cmcmc() fits supported generalized linear models using the built-in kernels. 'CUDA' is used when available, and an 'OpenMP'-enabled CPU backend is available on systems without a 'CUDA' compiler.

Somoclu

Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal . Details refer to (Peter Wittek, et al (2017)) .

Native GPU-Accelerated Simulation and Estimation of Network Models

A self-contained native engine (a C interface over 'CUDA' kernels and C++ host logic) for stochastic actor-oriented models (the model family of 'RSiena'), exponential random graph models (cross-sectional, temporal, and separable temporal), and models for binary actor attributes, callable from R without a Python runtime. Modelled on the 'torch' package: the CRAN build is CPU-only from source; the GPU path is compiled from source when a 'CUDA' toolkit is detected at configure time. The data preparation, host statistics ('RSiena' Appendix B conventions), and moment targets are validated bit-for-bit against the reference implementation and reproduce 'RSiena' targets on public datasets to machine precision; the estimators match 'RSiena', 'ergm', 'btergm', and 'tergm' on public benchmark models.

MultiDimensional Feature Selection

Functions for MultiDimensional Feature Selection (MDFS): calculating multidimensional information gains, scoring variables, finding important variables, plotting selection results. This package includes an optional CUDA implementation that speeds up information gain calculation using NVIDIA GPGPUs. R. Piliszek et al. (2019) .

Multi Precision Computing

Provides new data-structure support for multi-precision computing for R users. The package supports 16-bit, 32-bit, and 64-bit operations. To the best of our knowledge, 'MPCR' differs from the currently available packages in the following: 'MPCR' introduces a new data structure that supports three different precisions (16-bit, 32-bit, and 64-bit), allowing for optimized memory allocation based on the desired precision. This feature offers significant advantages in memory optimization. 'MPCR' extends support to all basic linear algebra methods across different precisions. Optional GPU acceleration via CUDA is available for 32-bit and 64-bit operations when CUDA Toolkit is detected during installation, while 16-bit operations are GPU-only and limited to matrix-matrix multiplication. 'MPCR' maintains a consistent interface with normal R functions, allowing for seamless code integration and a user-friendly experience.