Changes in version 0.1.0                        

  - herit_vc() and herit_batch() now return var_covariates: the
    proportion of phenotypic variance explained by fixed-effect
    covariates (R² on the INT-transformed phenotype). This corresponds
    to the "variance explained" column reported in Leocadio-Miguel et
    al. (2025, J Sleep Res). Returns NA for unadjusted models. sigma2_a
    and sigma2_e are now documented using SOLAR's sigma²_g / sigma²_e
    notation for clarity.

  - build_grm() — build an additive genetic relationship matrix from a
    pedigree data frame via kinship2::kinship(). Supports custom column
    names, graceful handling of missing parents, and informative errors
    for common mistakes.

  - herit_vc() — profile-likelihood variance-components heritability
    estimator for a single quantitative trait. Features: inverse-normal
    transformation, one-sided LRT with chi-squared(1) boundary
    correction, profile-likelihood 95% CIs, and zero-variance covariate
    detection.

  - herit_batch() — iterate herit_vc() over many traits x covariate
    model combinations; returns a tidy data frame. Includes a cli
    progress bar.

  - int_transform() — exported rank-based inverse-normal transformation
    (Blom-style).

  - plot_forest() — ggplot2 forest plot method for herit_batch() output,
    with optional model filtering and significance shading.