Silhouette 0.9.6 (October 2025)

New Features

• Introduced new functions:

• getSilhouette()
  Constructs a Silhouette class object directly from user-provided components without performing silhouette calculations. Allows users to build Silhouette objects when they already have cluster assignments, neighbor clusters, silhouette widths, and optional weights.

• is.Silhouette()
  Tests whether an object is of class “Silhouette” with optional strict validation. Checks both class inheritance and expected structure including required columns, attributes, and value ranges.

• cerSilhouette()
  Computes certainty silhouette widths for soft clustering using maximum posterior probabilities. Provides an alternative evaluation method for fuzzy clustering algorithms with support for crisp, fuzzy, and median averaging.

• dbSilhouette()
  Computes density-based silhouette widths using log-ratios of posterior probabilities based on Menardi (2011) method. Offers normalized silhouette evaluation for soft clustering with enhanced discrimination between cluster assignments.

• calSilhouette()
  Computes all available silhouette indices from the package functions and returns a comparative summary data frame. Automatically calculates crisp, fuzzy, and median silhouette values across different methods including proximity-based (medoid, pac), soft silhouette variations (pp_pac, pp_medoid, nlpp_pac, nlpp_medoid, pd_pac, pd_medoid), and probability-based methods (cer, db). Supports direct matrix input or clustering function output for streamlined comparative analysis.

Improvements

• Extended existing functions to support median-based silhouette calculations as an alternative to mean-based computations.
• Added new average attribute for existing Silhouette class supporting “crisp”, “fuzzy”, and “median” options.
• Enhanced clustering evaluation capabilities with density-based and certainty-based statistical measures.
• Updated documentation to reflect new median calculation options and soft clustering methods.
• Enhanced calSilhouette() to compute all combinations of softSilhouette() methods (pp, nlpp, pd) with both pac and medoid approaches, providing comprehensive comparative analysis across 11 different silhouette computation methods.

Updated Dependencies

• Imports: dplyr, ggplot2, ggpubr, lifecycle, methods, stats
  
• Suggests: proxy, ppclust, blockcluster, cluster, factoextra, drclust

Silhouette 0.9.5 (August 2025)

• Improved documentation:
  • Updated README.md and DESCRIPTION fields.
  • Fixed an example in the vignettes for clarity.
• Added lifecycle badges to all exported functions.

Silhouette 0.9.4 (July 2025)

CRAN Suggested Corrections

• Clarified the description of the PAC (Probability of Alternative Cluster) method in the documentation by citing the original authors (Author, Year).
• Removed usage of installed.packages() from the package code and replaced it with a more robust and CRAN-compliant approach, following suggested practices.
• All user-facing functions now include the argument print.summary, which controls whether summary details are printed. This argument behaves similarly to if (verbose) cat(...) and now defaults to FALSE.
• Messages using message() are included in example sections to suggest installation of optional packages (e.g., ppclust, blockcluster). These packages are not called directly, but their outputs are used in examples. This is done to guide users without violating CRAN’s policy on requiring packages in examples.
• Removed the license file as suggested.

Silhouette 0.9.3 (July 2025)

• Removed Citation file from inst folder and added WORDLIST in it.
• Modified Description and Title in DESCRIPTION file.

Silhouette 0.9.2 (July 2025)

Initial Stable Release

The first stable release of the Silhouette package, offering tools to compute and visualize silhouette widths for evaluating clustering quality. It supports both crisp and soft clustering outputs, along with multi-way clustering (e.g., biclustering).

Key Features

• Silhouette()
  Computes silhouette widths using proximity matrices (dissimilarity or similarity). Supports "medoid" and "pac" normalization methods, and fuzzy silhouette calculation via membership probabilities.

• softSilhouette()
  Tailored for soft clustering. Transforms membership probabilities into proximity measures (posterior probabilities, negative log-probabilities, or raw distributions) before silhouette computation.

• extSilhouette()
  Extends silhouette analysis to multi-way clustering (e.g., biclustering or triclustering) by aggregating silhouette widths across modes, weighted by observation counts.

• plotSilhouette()
  Creates detailed silhouette plots using ggplot2, with options for grayscale themes, cluster-wise summaries, and labeled observations. Compatible with outputs from Silhouette, softSilhouette, and popular packages like cluster and factoextra.

• summary.Silhouette()
  Summarizes silhouette results, including average silhouette widths per cluster and overall summary statistics.

Dependencies

• Imports: dplyr, ggplot2, ggpubr, methods
  
• Suggests: proxy, ppclust, blockcluster, cluster, factoextra