Changes in version 0.2.0 (2026-10-21)                  

First release of the R package. The repository now holds three packages
that follow one specification (docs/algorithm.md) and pass one
conformance suite (conformance/): the R package cldreducer at the
repository root, the Python package cld-reducer in python/, and the
JavaScript package cld-reducer in js/. All three solve the problem with
HiGHS and return the same display for the same input.

R package

  - reduce_letters() reduces a compact letter display from pairwise
    comparisons, and reduce_from_adjacency() does the same from a matrix
    of non-significant pairs. Both return a cld_reduction object with
    print() and as.data.frame() methods.
  - The example data sets are piepho2004_wheat, simple_abc_pairs, and
    simple_abc_means.
  - R turns HiGHS presolve off, because the highs package bundles
    HiGHS 1.14.

JavaScript package

  - New npm package cld-reducer in js/, built on HiGHS compiled to
    WebAssembly: reduceLetters(), reduceFromAdjacency(), loadSolver(),
    and the error classes CldReducerError, InvalidInputError, and
    SolverError. Group means must be a Map or an array of { group, mean
    }; a plain object is rejected.

Python package

  - Preserve exact CSV labels, including leading zeros, NA-like text,
    and empty strings.

  - Preserve mixed numeric labels in lists of pair rows and means
    mappings.

  - Use one means-column rule for group order and values.

  - Report uneven adjacency rows as InvalidInputError with the
    square-matrix message.

  - The package moved to python/. The import name cld_reducer, the
    public names, the exceptions, and the cld-reduce command with its
    flags are unchanged. Installing from a git URL now needs
    #subdirectory=python: pip install
    "git+https://github.com/aigorahub/cld-reducer.git#subdirectory=python".

  - The solver is HiGHS through highspy. The dependencies are highspy,
    numpy, and pandas; scipy and networkx are gone.

  - Canonical tie-break. Many inputs have several displays with the
    same, smallest number of assignments (the Piepho 2004 wheat example
    has 64). The package now returns one fixed display: among all
    minimal displays, the one whose membership vector is
    lexicographically greatest in the order of the specification. The
    result no longer depends on which optimum the solver finds first, so
    R, Python, and JavaScript agree. For inputs with several minimal
    displays this can differ from version 0.1.0.

  - Canonical clique order. Maximal cliques are now ordered by their
    sorted group indices, where 0.1.0 used the order of NetworkX. This
    order breaks ties between letters, so the letters can be renamed
    even for inputs that have only one minimal display. Example: five
    groups numbered 0 to 4, no means, and non-significant
    pairs 01, 02, 03, 04, 14, 24 have one minimal display; group 3 had A
    in 0.1.0 and has C now.

  - stats["reduction_pct"] is not rounded any more; print it with the
    format you need. The example scripts print one decimal.

  - stats["solver_status"] is the text Optimal, and stats["objective"]
    is a whole number.

  - time_limit is one time budget for all solves of a call. Each solve
    gets the time that is left.

  - Group means must be finite numbers; a missing, infinite, or
    non-numeric mean is an InvalidInputError.

  - A missing group label (None, NaN, pd.NA, or pd.NaT) is an
    InvalidInputError, in the comparisons, the groups, and the means.

  - Means with a repeated group label are an InvalidInputError for
    reduce_from_adjacency() too.

  - Significance text is trimmed of spaces, tabs, carriage returns, and
    line feeds only. Other white space, such as a no-break space, makes
    the value invalid.

  - An empty list of comparisons is a table with zero rows, so the
    groups come from the means.

  - A solver value that is not within 1e-6 of 0 or 1 is a SolverError,
    also when the value is a whole number.

Release preparation

  - Check installed Python wheels, wheels rebuilt from source
    distributions, and npm TypeScript consumers outside the checkout.
  - Require manual tag-based publication with dry runs, version checks,
    artifact hashes, and separate npm/PyPI jobs.
  - Record data reuse conditions and Python publication history as open
    release requirements.