dbrobust 1.1.0

New Features

• Robust Covariance & MCD Integration: Integrated the new Fast Minimum Covariance Determinant (mcd) estimator as a robust alternative to Geometric Variability (gv) for handling continuous variables within robust_distances(). This includes full stochastic control and iterational parameter tuning (niter, nstart).
• Multi-Source Distance Combination (merge_distances): Implemented merge_distances(), a generalized multi-source distance combination function based on multidimensional Related Metric Scaling (RelMS) methodology. It dynamically fuses multiple distance matrices (sources) while penalizing and discarding shared redundant information across sources.
• Cramér’s V Categorical Distance Framework: Enhanced internal categorical distance calculations (dist_categorical()) with support for weighted Cramér’s V association matrices (method = "cramer"), enabling Mahalanobis-type distance projections that down-weight redundant categorical attributes.

Performance & Internal Improvements

• Hybrid Rcpp Engine for RelMS: Integrated Rcpp and RcppArmadillo into the package core to optimize Related Metric Scaling in merge_distances(). High-overhead combinatoric cross-product loops are now offloaded to compiled native C++ code linked against R’s BLAS and LAPACK libraries.
• Algebraic Memory Optimization: Refactored internal structural equations in merge_distances() to eliminate heavy diagonal scaling matrices (diag()), reducing spatial complexity from \(O(n^3)\) to \(O(n^2)\) via vectorized row/column scaling. Benchmarks show a consistent ~1.9x speedup under high-dimensional stress scenarios (\(m = 40\) sources), cutting execution times and memory footprint in half.
• Robust Eigenvalue Strategy in make_euclidean(): Reimplemented make_euclidean() using a hybrid eigenvalue computation strategy. Integrated RSpectra::eigs_sym() to compute only the smallest algebraic eigenvalue, backed by a robust tryCatch fallback to base eigen() in case of convergence failures or missing convergence warnings, ensuring 100% stability under extreme colinearity.
• Optimized internal matrix operations in the G-Gower algorithms by replacing standard matrix multiplications (%*% and t()) with highly optimized crossprod() and tcrossprod() functions.
• Enhanced internal plotting functions to fully support robust color mapping, allowing both standard positional color vectors and named character vectors for ultra-safe group color alignment.
• Updated plot_qgraph() internal logic to support advanced customization arguments, including explicit toggle control for group legends (show_legend) and custom edge/arc coloring (edge_color).

Bug Fixes

• Corrected Minkowski parameter in calculate_distances(): Standardized the Minkowski distance exponent argument to q (instead of p), updating internal signatures and documentation for consistency.
• Resolved default weight assignment in make_euclidean(): Fixed a documentation and functional mismatch where omitting the weight argument w was not properly documented as defaulting to uniform weights (\(w_i = 1/n\)).
• Fixed an isSymmetric error in make_euclidean() when processing objects of class D2 (from dbstats) or other custom distance classes. The function now ensures raw matrix coercion before symmetry checks.
• Fixed a bug where RSpectra unconverged outputs (numeric(0)) caused an argument has length zero error during Lingoes evaluation in make_euclidean().
• Fixed a bug in robust_distances() where binary and categorical variables provided as factors in a data.frame were incorrectly coerced, leading to inflated distance calculations.
• Fixed an issue in the Jaccard distance calculation for asymmetric binary variables where a union of zero (both observations lacking the trait) caused incorrect distance assignments. It now correctly evaluates to 0.
• Removed hidden non-ASCII characters (non-breaking spaces) from source code files to fully comply with CRAN cross-platform portability requirements.

Documentation & Testing

• Stochastic Reproducibility Testing: Expanded the testthat suite to comprehensively check the new MCD and RelMS components, enforcing strict reproducibility boundaries via isolated seeds (set.seed).
• Fully updated Roxygen2 documentation for network plotting internals (plot_qgraph), providing comprehensive examples demonstrating custom named vector coloring and layout optimization.
• Enhanced make_euclidean documentation with detailed explanations of performance optimizations and hybrid eigenvalue selection logic.
• Added detailed examples and unit tests for merge_distances() covering multi-domain datasets, high-source stress tests (\(m = 50\)), and exact distance matrix combinations.

dbrobust 1.0.0

• Initial release to CRAN.