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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.

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.