NEWS
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