simOutrank 0.3.0

• Breaking: the quantile-threshold constructor q() is renamed to as_quantile() to avoid masking base::q() and for consistency with as_traces(). Replace q(0.8) with as_quantile(0.8).
• Added the maintainer’s ORCID.

simOutrank 0.2.0

Data

• illustrative_log now ships the authoritative CSV from Delias et al. (2023) rather than a reconstruction of Table 1. Breaking: case ids are now "1"–"25" (previously G1/B1…), the lower tier is "NORMAL" (previously "Blue"), and satisfaction is "High"/"Low" (previously upper case). The credibility matrix S is unchanged in value.

Vignettes

• The reproduction vignettes use the real illustrative log. The large-log article now reproduces the BPIC’11 case study of Delias et al. (2023), including Figures 2 and 3 from bundled aggregate tables.

Internal

• Added gated (skip_on_cran) regression tests on the BPIC’11 similarity matrix. That matrix and the preprocessed BPIC’11 log are kept local-only under the 4TU General Terms of Use and are not redistributed with the package.

simOutrank 0.1.0

First public release. simOutrank implements outranking-based trace clustering for process mining.

Pipeline

• as_traces() turns a data.frame event log (or, with , an eventlog/activitylog) into a traces object.
• Criterion helpers build the pairwise measures: process-aware (crit_activity_profile(), crit_transitions(), crit_edit_distance(), crit_trace_length(), crit_distinct_activities(), crit_duration()), attribute templates (crit_nominal(), crit_ordinal(), crit_numeric()) and the crit_custom() escape hatch, all built on criterion() with fixed-number or quantile (as_quantile()) thresholds.
• outrank_similarity() aggregates the criteria into the ELECTRE-III credibility matrix S.
• cluster_traces() partitions S by normalized spectral clustering (Ng–Jordan–Weiss) or hierarchical clustering; eigengap() helps choose k.

Robustness and diagnostics

• trim_outliers() (greedy and exact integer-program methods).
• must_link() / cannot_link() pairwise constraints.
• validate_clusters() (connectivity and Dunn index via ), augment_log() and plot() methods.

Data and documentation

• Bundled illustrative_log dataset (Delias et al., 2023, Table 1).
• Vignettes: getting started, reproducing the illustrative example, robustness, and parameter tuning; plus website articles reproducing the emergency-room study and clustering a large log.