NEWS

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AddiVortes 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.

AddiVortes 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.

AddiVortes 0.5.4

• Added data to package to help with vignettes and testing.

AddiVortes 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.

AddiVortes 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).

AddiVortes 0.5.1

• Reinstated C++ speed-ups for new distance metrics
• Restructured testthat tests

AddiVortes 0.5.0

• Added compatibility for purely spherical input data and mixed Euclidean and Spherical inputs.

AddiVortes 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).

AddiVortes 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.

AddiVortes 0.4.10

• Fixed variance/standard deviation mismatch in tessellation proposal step.
• Fixed typo in variable names and added more camelCase.

AddiVortes 0.4.9

• Improved legends in plot.AddiVortesFit().
• Fix typos in package description.

AddiVortes 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 <cstring> header for memcpy() function in C++ code
  • Clang compiler requires explicit inclusion of standard library headers

AddiVortes 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

AddiVortes 0.4.6

• Linting and formatting improvements.
• Final preparations for CRAN submission.

AddiVortes 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).

AddiVortes 0.4.4

• Fixed test automation bug stemming from too many cores being assumed available.

AddiVortes 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.

AddiVortes 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.

AddiVortes 0.4.1

• Added parallel processing to predict function.
• Removed unnecessary square-root function from KD-tree search.

AddiVortes 0.3.3

• Added warning for situation where covariates exceed observations.

AddiVortes 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.

AddiVortes 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

AddiVortes 0.3.0

• Implemented nearest neighbour search in C++.
• Removed dependency on FNN package.

AddiVortes 0.2.5

Package Features

• Added progress bars to MCMC sampling functions and subsequent prediction functions for better user feedback during long computations.

AddiVortes 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