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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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.