v1.2.0

New features

• Added KL divergence (calculate_kl_divergence()) and Hellinger distance
  (calculate_hellinger_distance()) metrics for evaluating cluster distribution
  consistency between training and held-out subjects.
• Added modularity score (calculate_modularity()) computed on the precomputed
  SNN graph.
• Added MSE and MAD scores (calculate_mse_score()) for centroid-based cluster
  quality evaluation.
• New suggest_resolution() function that ranks resolutions using two
  complementary methods: direct rank aggregation across four metrics, and
  curvature-based local optima detection via second-order finite differences.
• New summarize_cv_metrics() for per-resolution metric summaries.
• New visualization functions plot_rank_metrics() and plot_mean_rank().
• Logging improvements to make clustOpt messages distinct from its dependencies.

Performance improvements

• Pre-allocate the results list in the main cross-validation loop instead of
  growing it with c(), reducing memory allocation overhead for many subjects.
• Use crossprod() instead of explicit t() %*% in PCA projections, avoiding
  materialization of transposed gene-by-cell matrices.
• Use Matrix::crossprod() in modularity calculation to stay in sparse matrix
  space and avoid dense transposition of the cluster indicator matrix.
• Replace do.call(rbind, lapply(...)) with t(vapply(...)) in MAD
  calculation to avoid intermediate list allocation.
• Precompute SNN graph and distance matrix once per held-out subject (shared
  across resolutions) instead of recomputing per resolution.
• Pre-extract cluster assignments from metadata by resolution to avoid repeated
  column lookups.

Logging and verbosity

• verbose parameter now accepts integer levels (0-3) for fine-grained control:
  0 = silent, 1 = key milestones, 2 = detailed progress, 3 = Seurat output.
• Backward-compatible: verbose = TRUE maps to level 1, FALSE to 0.
• Added per-step timing via [step_name] Xs log messages at verbose >= 1.

Bug fixes

• Fixed flipped sign in KL divergence calculation.
• Fixed ARI estimate for comparing singleton clusters.
• Handled edge cases in sample validation and metric computation.

Package reorganization

• Split monolithic clustOpt.R and utils.R into focused modules:
  clust_opt.R, data_preparation.R, metrics.R, sketching.R,
  validation.R, visualization.R.
• Removed vignette build cache from version control.

v1.0.0

First major release of clustOpt!