Changes in version 0.1.0                        

First release.

  - Core (base R): simulate_population(), qc_markers(),
    impute_markers(), Gmatrix() (VanRaden), and gblup() (GBLUP by REML,
    validated against rrBLUP::mixed.solve).
  - Unified modelling interface gs_fit() / predict() covering GBLUP,
    elastic net (glmnet), random forest (ranger) and gradient boosting
    (xgboost).
  - gs_cv() for breeding-relevant cross-validation (random k-fold and
    leave-one-group-out).
  - gs_ensemble(), a stacked super-learner combining base models with
    non-negative, out-of-fold-fitted weights.
  - gs_benchmark() with print/summary/plot to compare all available
    models under one cross-validation.