0.3.0

ew features: lattice navigation, concept measures, iceberg lattices, TikZ/Graphviz export, context operations, data-quality tools and an outlier detector. Documentation is now at https://conceptflow.readthedocs.io.

Added

• Documentation site with a user guide, API reference and changelog; every guide example is run by the test suite.
• Lattice navigation: join, meet, upset, downset, atoms, coatoms, object_concept, attribute_concept, concept_from_objects, concept_from_attributes.
• Iceberg lattices: min_support= computes only frequent concepts.
• Size guard: a LargeLatticeWarning past 50,000 concepts, and an opt-in max_concepts= limit.
• Concept measures: stability (exact and bounds), Δ-measure, entropy, information gain, linearity and distributivity indices; ConceptLatticeEstimator(select=..., n_concepts=...).
• Minimal generators and the proper-premise basis.
• Context handling: transpose, subcontext, rename, add/remove objects and attributes, full reduction, JSON (FCART/fcapy) and CSV files.
• Context operations: apposition, subposition, direct sum and product, complement, dual; nested diagrams of any two contexts.
• Layouts and export: layered layout, TikZ/LaTeX and Graphviz export.
• Data quality: check_implications, redundancy_report, explain_object, explain_rule.
• ImplicationOutlierDetector: a scikit-learn outlier detector that explains each flag with the rules it breaks.

Changed

• Cell values follow one rule per cell: numbers > 0 are crosses; strings must be a known spelling ("yes"/"no" now raise instead of being guessed).
• Out-of-range object/attribute indices raise IndexError.
• Estimators fitted on named columns refuse different or reordered columns, as scikit-learn's do.

Full details: CHANGELOG

pip install --upgrade conceptflow