Changes in version 0.1.0                        

First CRAN release

New functions

  - check_outliers() — univariate outlier detection (IQR fence, Z-score,
    Hampel identifier, Grubbs, Dixon Q-test) with consensus flagging. S3
    print and plot methods included.
  - check_outliers_mv() — Mahalanobis distance multivariate outlier
    detection with optional robust (MCD) covariance and ridge
    regularisation for near-singular matrices.
  - check_missing() — missing data analysis with Little's MCAR test
    (internal implementation), pattern matrix, mechanism classification,
    and missingness heatmap.
  - classify_missing() — per-variable logistic regression to classify
    missingness as MCAR, MAR, or MNAR.
  - check_normality() — battery of six normality tests (Shapiro-Wilk,
    Anderson-Darling, Kolmogorov-Smirnov, Lilliefors, Pearson
    chi-square, Jarque-Bera) with skewness/kurtosis diagnostics and
    consensus recommendation. Jarque-Bera is implemented internally with
    no external dependency.
  - check_homogeneity() — Bartlett, Levene (Brown-Forsythe, center =
    median), and Fligner-Killeen tests for homogeneity of variance with
    practical variance ratio.
  - check_independence() — Durbin-Watson, Breusch-Godfrey, and
    Wald-Wolfowitz runs tests for independence of residuals, with ACF
    plot.
  - check_design() — experimental design validation for CRD, RCBD, LSD,
    and factorial designs: balance, completeness, error df (Gomez &
    Gomez guideline), and missing treatment combinations.
  - check_qualitative() — categorical variable quality checks: case
    inconsistency, whitespace, near-duplicate labels (Levenshtein, long
    labels only), unexpected levels, and rare categories.
  - standardise_labels() — automatic label standardisation with case
    conversion and lookup-table replacement.
  - run_dq_pipeline() — single-call full pipeline runner returning a
    master summary data frame.
  - generate_dq_report() — automated self-contained HTML report with
    green/amber/red scorecard.

Data

  - agri_trial — simulated RCBD wheat variety trial (20 plots, 4
    treatments T1-T4, 5 blocks B1-B5) with one intentional outlier and
    one missing value for demonstration.

Notes

  - All skewness, kurtosis, and Jarque-Bera calculations are implemented
    internally, eliminating the dependency on the moments package.
  - Grubbs and Dixon Q-tests are implemented internally, eliminating the
    dependency on the outliers package.
  - Little's MCAR test is implemented internally, eliminating the
    dependency on BaylorEdPsych or MissMech.
  - The package requires R >= 4.1.0.