Changes in version 0.6.1 - AddiVortes now supports covariates drawn from multiple subspaces of the same type and allows future support for more complex categorical covariate distance metrics; AddiVortes now takes a members argument as well as the metric argument to identify different covariate subspaces. - Distance calculation has been streamlined and modularised via data preprocessing in covariateStructure and associated changes in C++ code. - Package dependency has been updated to explicitly require C++20 compilation. Changes in version 0.6.0 - Consolidated model fitting into a single C++ call (addi_vortes_mcmc_cpp). The complete MCMC loop — sigma sampling, tessellation proposals, cell-index assignment, residual aggregation, acceptance-probability evaluation, and mu sampling — now runs entirely in C++, eliminating the per-iteration R↔C++ overhead that was present in previous versions. - The AddiVortes class and all associated S3 methods are unchanged. Changes in version 0.5.4 - Added data to package to help with vignettes and testing. Changes in version 0.5.3 - Added vignette "Modelling Spherical Data with AddiVortes" (by Andy Iskauskas and John Paul Gosling) demonstrating the use of great-circle distance via metric = "S", the coordinate convention (latitude/longitude in radians), and a comparison with the Euclidean metric on synthetic globe data. - Added vignette "Using Categorical Covariates with AddiVortes" explaining automatic one-hot encoding (d categories → d-1 binary indicators), the catScaling parameter, the catEncoding metadata field, and handling of unseen category levels at prediction time. Changes in version 0.5.2 - Added support for categorical covariates via automatic one-hot encoding. Character and factor columns are converted to d-1 binary indicator variables (first level as reference), controlled by the new catScaling parameter. - predict.AddiVortes() now accepts data.frame inputs when the model was trained with categorical covariates, applying the stored encoding automatically. - Unseen categories at prediction time are mapped to the reference level (all zeros). - Encoding metadata stored in the catEncoding field of the fitted model object. - Added comprehensive tests for categorical covariate support. - Added second example demonstrating categorical covariates with a larger training (n=200) and test set (n=50). Changes in version 0.5.1 - Reinstated C++ speed-ups for new distance metrics - Restructured testthat tests Changes in version 0.5.0 - Added compatibility for purely spherical input data and mixed Euclidean and Spherical inputs. Changes in version 0.4.12 - Added comprehensive test suite for AddiVortes class methods syntax validation. - Tests cover input validation and error handling for all S3 methods (print, summary, predict, plot). Changes in version 0.4.11 - Renamed class AddiVortesFit to AddiVortes to maintain consistency with the package name. - Updated all S3 methods (print, summary, predict, plot) to use the new class name. - Updated constructor function from new_AddiVortesFit to new_AddiVortes. Changes in version 0.4.10 - Fixed variance/standard deviation mismatch in tessellation proposal step. - Fixed typo in variable names and added more camelCase. Changes in version 0.4.9 - Improved legends in plot.AddiVortesFit(). - Fix typos in package description. Changes in version 0.4.8 (2026-01-14) - Initial CRAN release of AddiVortes package. - Fixed installation failure on r-devel-linux-x86_64-fedora-clang: - Added missing header for memcpy() function in C++ code - Clang compiler requires explicit inclusion of standard library headers Changes in version 0.4.7 (2026-01-13) - Fixed DESCRIPTION file for CRAN compliance: - Removed non-standard 'Keywords' field - Removed redundant 'Author' and 'Maintainer' fields (now auto-derived from Authors@R) - Resolved R CMD check NOTEs for DESCRIPTION meta-information Changes in version 0.4.6 - Linting and formatting improvements. - Final preparations for CRAN submission. Changes in version 0.4.5 - Fixed bug in tessellation proposal when number of covariates equals number of selected dimensions. - Add Dimension (AD) modification now properly checks if all covariates are already selected before attempting to add a new one. - Added comprehensive test suite for small covariate counts (1, 2, and 3 covariates). Changes in version 0.4.4 - Fixed test automation bug stemming from too many cores being assumed available. Changes in version 0.4.3 - Cleaned up tests folder for CRAN submission preparation. - Removed TestSuite.R, CodeProfiler.R, and TestHelper.R from tests directory. - Incorporated relevant tests from TestSuite.R into testthat framework. - Added test for thinning parameter functionality. - Only testthat.R remains in tests folder alongside the testthat directory per CRAN requirements. Changes in version 0.4.2 - Added prediction interval support to predict.AddiVortesFit() function with new interval parameter. - Fixed bug where posteriorSigma was not stored in model objects. - Prediction intervals now available alongside confidence intervals, similar to lm predict function. - Updated tests to use named access instead of numeric indexing for improved robustness. Changes in version 0.4.1 - Added parallel processing to predict function. - Removed unnecessary square-root function from KD-tree search. Changes in version 0.3.3 - Added warning for situation where covariates exceed observations. Changes in version 0.3.2 - Improved progress bar output to give more information during MCMC sampling and prediction processes. - Removed outputted progress bars from vignette examples to reduce clutter. Changes in version 0.3.1 - Enhanced SEO and discoverability with comprehensive keyword optimization across package documentation - Added formal Keywords field in DESCRIPTION for better search indexing - Updated README.md with clear positioning as BART alternative - Optimized vignette titles for machine learning and Bayesian regression search terms - Enhanced package documentation with machine learning focus Changes in version 0.3.0 - Implemented nearest neighbour search in C++. - Removed dependency on FNN package. Changes in version 0.2.5 Package Features - Added progress bars to MCMC sampling functions and subsequent prediction functions for better user feedback during long computations. Changes in version 0.2.4 CRAN Submission Preparation - Removed compiled object files from package source - Added examples to main AddiVortes() function - Updated DESCRIPTION file for CRAN compliance: - Added Author and Maintainer fields - Added R version dependency (>= 3.5.0) - Updated Date field - Fixed grammatical error in Description - Updated .Rbuildignore with standard exclusions - Enhanced documentation with examples for key functions Package Features - Implements Bayesian Additive Voronoi Tessellation models - Non-parametric regression with tessellation-based approach - Posterior sampling via backfitting algorithm - Prediction and visualization methods for fitted models - Comprehensive test suite and vignettes