Planned for the next release (0.2.0):
gwas() performs a single-marker genome-wide association scan with optional
principal-component structure correction, returning a gs_gwas object with
print() and plot() methods (Manhattan and QQ plots).Dmatrix() builds the dominance genomic relationship matrix (Vitezica et al.
2013), which can be passed to gblup() as K to fit a dominance GBLUP model.First release.
simulate_population(), qc_markers(), impute_markers(),
Gmatrix() (VanRaden), and gblup() (GBLUP by REML, validated against
rrBLUP::mixed.solve).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.