High-Performance Open-Source Archive
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).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.dist_categorical()) with support for weighted Cramér’s V
association matrices (method = "cramer"), enabling
Mahalanobis-type distance projections that down-weight redundant
categorical attributes.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.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.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.%*% and
t()) with highly optimized crossprod() and
tcrossprod() functions.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).calculate_distances(): Standardized the Minkowski
distance exponent argument to q (instead of
p), updating internal signatures and documentation for
consistency.make_euclidean(): Fixed a documentation and
functional mismatch where omitting the weight argument w
was not properly documented as defaulting to uniform weights (\(w_i = 1/n\)).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.RSpectra unconverged outputs
(numeric(0)) caused an
argument has length zero error during Lingoes evaluation in
make_euclidean().robust_distances() where binary and
categorical variables provided as factors in a data.frame
were incorrectly coerced, leading to inflated distance
calculations.testthat suite to comprehensively check the new MCD and
RelMS components, enforcing strict reproducibility boundaries via
isolated seeds (set.seed).plot_qgraph), providing comprehensive examples
demonstrating custom named vector coloring and layout optimization.make_euclidean documentation with detailed
explanations of performance optimizations and hybrid eigenvalue
selection logic.merge_distances() covering multi-domain datasets,
high-source stress tests (\(m = 50\)),
and exact distance matrix combinations.
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