Learning Bayesian Networks with Mixed Variables

Bayesian networks with continuous and/or discrete variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), .

Borenstein Analysis

An implementation of the analysis about seed components from Borenstein et.al. 2008.

Presentation Ninja

Create HTML5 slides with R Markdown and the JavaScript library 'remark.js' (< https://remarkjs.com>).

Component-Wise MOEA/D Implementation

Modular implementation of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) [Zhang and Li (2007), ] for quick assembling and testing of new algorithmic components, as well as easy replication of published MOEA/D proposals. The full framework is documented in a paper published in the Journal of Statistical Software [].

Build 'Rust' Powered 'R' Packages

Provides a framework for creating high-performance 'R' packages powered by the 'Rust' programming language using the 'extendr' Rust crate. It offers 'usethis'-like functions to scaffold and develop 'Rust' powered 'R' packages, including utilities for publishing to CRAN, managing dependencies, configuring development environments, and rendering 'Rust' code in 'knitr' documents. Additionally, it provides functions to compile and evaluate 'Rust' code directly from 'R' for interactive development.

Create Visualisations for BART Models

Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) model fits. We construct conventional plots to analyze a model’s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our visualisations are designed to work with the most popular BART R packages available, namely 'BART' Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 , 'dbarts' (Vincent Dorie 2023) < https://CRAN.R-project.org/package=dbarts>, and 'bartMachine' (Adam Kapelner and Justin Bleich 2016) .

Calculates the Bidimensional Regression Between Two 2D Configurations

Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965).

Bayesian Statistics for 2D/3D Transformations

Fits 2D and 3D geometric transformations via 'Stan' probabilistic programming engine ( Stan Development Team (2021) < https://mc-stan.org>). Returns posterior distribution for individual parameters of the fitted distribution. Allows for computation of LOO and WAIC information criteria (Vehtari A, Gelman A, Gabry J (2017) ) as well as Bayesian R-squared (Gelman A, Goodrich B, Gabry J, and Vehtari A (2018) ).

'OpenCL'-Ported R 'Mathlib' for GPU-Accelerated Packages

Ships statistical and mathematical routines from R internal 'nmath' ('Mathlib') as 'OpenCL' C sources under directory 'inst/cl/', with R wrappers that use the GPU when 'OpenCL' is available at compile time and fall back to 'stats' equivalents otherwise. Aimed at package developers building custom kernels (for example Bayesian GLMs via suggested package 'glmbayes') using 'opencltools' kernel loaders and related helpers. Contains translated shims, an illustrative GLM-related kernel subsystem, vignettes, and optional GPU acceleration. The ported routines are translated from the 'nmath' ('Mathlib') and 'Rmath' sources of R Core Team (2026) "R: A Language and Environment for Statistical Computing" . 'OpenCL' GPU execution follows the standard described in Stone, Gohara, and Shi (2010) . The likelihood subgradient simulation methodology implemented by the illustrative GLM kernel subsystem is described in Nygren and Nygren (2006) .

A Common Representation of Time Indices of Different Types

Provides an easy-to-use tind class representing time indices of different types (years, quarters, months, ISO 8601 weeks, dates, time of day, date-time, and arbitrary integer/numeric indices). Includes an extensive collection of functions for calendrical computations (including business applications), index conversions, index parsing, and other operations. Auxiliary classes representing time differences and time intervals (with set operations and index matching functionality) are also provided. All routines have been optimised for speed in order to facilitate computations on large datasets. More details regarding calendars in general and calendrical algorithms can be found in "Calendar FAQ" by Claus Tøndering < https://www.tondering.dk/claus/calendar.html>.