Changes in version 0.6.2 (2026-09-01)                  

  - plot_biplot() no longer depends on factoextra. It's now a
    self-contained reimplementation of
    fviz_pca_biplot()/fviz_mca_biplot()'s geometry (individual
    points/arrows, variable arrows or category points, axis percentage
    labels, dashed origin lines, the r * 0.7 variable-arrow scaling
    rule, and the exact cos2/contribution formulas factoextra uses for
    prcomp/FactoMineR::MCA objects), verified to reproduce numerically
    identical coordinates. factoextra dropped from Suggests; ggrepel
    added to Imports (previously an indirect factoextra dependency, now
    used directly for repelled labels).

                        Changes in version 0.6.1                        

  - Standardized figure colors package-wide. theme_phynotype() now
    builds on ggplot2::theme_classic() with an Okabe-Ito colorblind-safe
    palette (matching the funcml package's theme_funcml()), and new
    scale_color_phynotype()/scale_fill_phynotype() discrete scales are
    applied to every plot with a categorical color/fill mapping (cluster
    embedding, consensus clusters, feature profiles, silhouette widths).
    Previously these fell back to ggplot2's default hue palette.
  - plot(ceteris_paribus) no longer uses its own ad hoc teal/purple
    colors (visually indistinguishable from the DALEX/ingredients
    package's ceteris-paribus plot style, despite no dependency on it) -
    it now uses the same blue/vermillion accent pair as the rest of the
    package.
  - plot_coassoc()'s co-association heatmap now uses a proper continuous
    viridis gradient instead of ggplot2's default blue gradient.
  - plot(lime_explanation)'s positive/negative bars now use a consistent
    green/vermillion pair instead of ggplot2's default hue palette.
  - The two independently hardcoded feature-importance/cluster-size bar
    colors are now sourced from the same shared accent constant.
  - README figures re-rendered to reflect the new palette.

                        Changes in version 0.6.0                        

  - Added plot_biplot(), a thin wrapper around
    factoextra::fviz_pca_biplot()/fviz_mca_biplot() for "pca" and "mca"
    embeddings. No FAMD or MDS equivalent, matching what factoextra
    itself provides. Three variants, all following factoextra's own
    documented examples: "cluster" (default, colored/shaped by cluster
    assignment), "cos2" (colored by quality of representation), and
    "label" (individuals shown as text instead of points).
  - Added theme_phynotype(), a single ggplot2 theme now used
    consistently across every plot function in the package (previously
    some used theme_minimal(), one used theme_bw(), and
    plot(ceteris_paribus) had its own ad hoc purple-tinted theme).
  - Fixed metacluster() so it can combine native mixed-data methods
    (kproto, kmm) with numeric-only (kmeans, pam, dbscan, gmm) or
    distance-based (hclust, agnes) methods on the same mixed-type
    dataset, by preparing per-method input instead of passing raw mixed
    data to every candidate.
  - Fixed validate.metacluster_fit() to encode mixed-type data before
    computing silhouette/Calinski-Harabasz/Davies-Bouldin, matching
    validate.cluster_fit().
  - All numeric package results now round to 4 significant digits by
    default: validate()'s metrics/per-cluster tables, explore()'s
    feature summary/separation/prototype tables, centers()/prototypes(),
    feature_importance(), ceteris_paribus(), lime_explain(), and
    metacluster()'s selection/stability summaries.
  - First CRAN submission.

                        Changes in version 0.0.9                        

  - Validation tables now include metric scale and preferred direction
    metadata for silhouette, bootstrap ARI, Davies-Bouldin, total
    within-cluster sum of squares, and Calinski-Harabasz.
  - plot_validation() now facets metrics into individual panels instead
    of combining incompatible scales on one axis.
  - Added the initial cluster() workflow with kmeans, pam, hclust,
    agnes, dbscan, and gmm wrappers.
  - Added metacluster() with co-association consensus clustering.
  - Added validate(), explore(), predict(), and core plotting helpers.
  - Added package tests, documentation, and starter vignettes.