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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).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.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).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).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.validate.metacluster_fit() to encode mixed-type
data before computing silhouette/Calinski-Harabasz/Davies-Bouldin,
matching validate.cluster_fit().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.plot_validation() now facets metrics into individual
panels instead of combining incompatible scales on one axis.cluster() workflow with
kmeans, pam, hclust,
agnes, dbscan, and gmm
wrappers.metacluster() with co-association consensus
clustering.validate(), explore(),
predict(), and core plotting helpers.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.