Package index
Across-environment allocation
Functions for distributing treatments across environments and for verifying the feasibility and balance of the resulting incidence structure.
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allocate_sparse_met() - Allocate test treatments across environments for sparse MET designs
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check_balanced_incomplete_feasibility() - Evaluate feasibility of an exact balanced incomplete sparse MET allocation
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derive_allocation_groups() - Derive allocation group labels for sparse MET treatment assignment
Feasibility and capacity helpers
Pre-flight diagnostics to verify that the chosen per-environment capacity is sufficient to assign every non-common treatment at least once before allocation begins.
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feasibility_helpers - Feasibility helpers for sparse MET allocation
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min_k_for_full_coverage() - Compute the minimum per-environment capacity for full treatment coverage
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suggest_safe_k() - Suggest a safe uniform per-environment capacity for sparse MET allocation
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warn_if_k_too_small() - Warn when per-environment capacity is insufficient for full treatment coverage
Seed-aware replication planning
Partition treatments into replicated, unreplicated, and excluded roles based on available seed quantities and per-plot seed requirements.
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assign_replication_by_seed() - Classify treatments into replication roles based on seed availability
Within-environment field design
Construct field layouts within each environment. Two engines are available: block-based repeated-check designs and alpha row-column stream designs.
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met_prep_famoptg() - Construct a repeated-check block design with flexible replication
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met_alpha_rc_stream() - Construct a stream-based repeated-check alpha row-column design
Efficiency evaluation
Evaluate the statistical efficiency of within-environment designs under A-optimality, D-optimality, and CDmean criteria before field deployment.
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met_evaluate_famoptg_efficiency() - Evaluate the statistical efficiency of a repeated-check block design
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met_evaluate_alpha_efficiency() - Evaluate the statistical efficiency of an alpha-lattice design
Design optimisation
Search for higher-efficiency field arrangements using criterion-driven optimisation. Supports Random Restart, Simulated Annealing, and Genetic Algorithm methods.
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met_optimize_famoptg() - Search for a criterion-optimal repeated-check block design
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met_optimize_alpha_rc() - Search for a criterion-optimal alpha-lattice design
Pipeline and assembly
End-to-end pipeline orchestration and assembly of environment-level field books into a single MET-level field book.
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plan_sparse_met_design() - Plan a sparse multi-environment trial design and assemble a combined field book
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combine_met_fieldbooks() - Combine environment-level field books into a single MET field book
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OptiSparseMET_example_data - Example data for OptiSparseMET