Changes in version 2.0.0 (2026-10-25)                  

This release preserves the public R interfaces, corrects numerical and
prediction errors, and improves documentation and validation. Both the R
package and Python interface use version 2.0.0.

Dependencies and packaging

  - Remove the unused fairness import that caused CRAN to archive DSLD
    when that dependency was archived in November 2025. The active
    fairml dependency remains. This change has not been submitted to
    CRAN.
  - Declare the optional cccp and CVXR solvers and explain how to
    install missing optional packages.
  - Add regression checks, documentation/example validation, and
    scheduled CI. Exclude developer artifacts and Python caches from the
    R source package.
  - Update the maintainer address to mittalaa@uci.edu, add the
    methods-paper citation, and document the bundled datasets'
    dimensions and data usage.

Correctness

  - Use the positive-class probability column when scoring binary random
    forests. Cross-validation no longer flattens both probability
    columns together.
  - Preserve yesYVal in binary matching and EDF predictions, including a
    positive label of "0"; report original class labels in EDF
    classification results.
  - Fix frequency-table column labels and preserve row alignment when
    sensitive attributes contain missing values. Undefined correlations
    remain NA.
  - Use fitted model matrices for linear prediction standard errors and
    sensitive group contrasts. Correct contrast ordering for four or
    more groups and avoid confusing similarly named covariates with the
    sensitive attribute.
  - Apply KNN scaling and deweights consistently at training and
    prediction; honor scaleX = FALSE, column reordering, and
    one-predictor data frames.
  - Correct linear ridge augmentation so its intercept is unpenalized.
    Retain the original pseudo-observation method for the logistic EDF
    variant.
  - Accept omitted deweights in both ridge variants, and preserve
    ordered-factor and custom-contrast encodings when predicting with
    fairml models.
  - Construct linear, logistic, and linear-ridge formulas from literal
    column names, allowing outcome names with spaces, operators, or
    backticks without interpreting them as expressions or emitting
    formula-parsing warnings.
  - Preserve explicit sensitive-factor contrasts in linear/logistic fits
    when all levels are observed, including their coefficient labels and
    values.
  - Reuse cross-validation folds across candidates, stratify binary
    outcomes, weight errors by test-fold size, and retain undefined
    group correlations.
  - Accept data-frame comparison points in dsldML; compare feature sets
    using a shared holdout in dsldTakeALookAround.
  - Fix density slider indexing, character group handling, plot ranges,
    scatter row limits, and binary-response validation in
    conditional-disparity plots. Drop unused factor levels and omit
    constant axes in parallel coordinates.
  - Detect parallel-coordinate indicators that are constant within a
    plotted group before density scaling; omit them with an explicit
    warning. This avoids a failure found on Linux with current R and
    dependency binaries.
  - Convert integer/character inputs for bnlearn and preserve optimizer
    warnings from fairml instead of suppressing them.

Documentation and compatibility

  - Preserve all 25 exported R function signatures and defaults, model
    families, datasets, and historical result-field names.
  - Clarify that trainAcc and testAcc are prediction errors, and that
    sensitive correlations are associations rather than universal
    fairness measures.
  - Refresh both READMEs, help pages, the function guide, and the Quick
    Start. Quick Start results now come from executable chunks.
  - Numerical results affected by the fixes above may change. Invalid
    inputs now produce earlier, clearer errors.

  - Preserve 42 public callable signatures and defaults; add optional
    final model_args support to dsldPyFairUtils.
  - Accept pandas and R data frames consistently, preserve option types,
    and call R generic methods directly without temporary variables in
    R's global workspace.
  - Preserve existing result fields and add explicit EDF
    class/probability fields, matching results, and common-variable
    rankings.
  - Return usable Plotly figures, honor static-plot display controls,
    and close graphics devices and delete temporary images even when
    rendering fails.
  - Correct notebook imports, dataset preparation, class labels, and
    train/test handling. Rebuilt examples use bundled data and
    reproducible random seeds.
  - Require Python 3.9 or newer and provide optional notebook/example
    dependencies.