ExperimentalDesignGeneratorandRandomiser-package
                        Package-level documentation.
as.data.frame.edgar_design
                        'as.data.frame' method: returns the underlying
                        'rows' data frame.
as_layout_frames        Access the layout view as a list of data frames
                        (one per section), with section names from
                        'layout_section_labels'. Returns NULL if the
                        design has no layout.
design_alpha            Generate an 'alpha' design.
design_cr               Generate a 'cr_eq' design.
design_cr_unequal       Generate a 'cr_uneq' design.
design_info             Convenience wrapper to fetch a design's
                        metadata (for programmatic introspection by
                        tests or downstream packages).
design_latin            Generate a 'latin' design.
design_rcb              Generate an 'rcb' design.
design_rcb_unequal      Generate an 'rcb_uneq' design.
design_split_plot       Generate a 'split_plot' design.
design_two_factor_rcb   Generate a 'two_factor_rcb' design.
design_variable_blocks
                        Generate a 'variable_blocks' design.
edgar_py_random         Constructor: returns an environment that owns
                        the MT state. Pass by reference lets
                        'genrand_uint32' advance the state without
                        copying.
generate_design         Public dispatcher: generate a design by key.
has_layout              Whether the design carries a layout view.
list_designs            List all registered designs with their
                        metadata.
make_rng                Create a fresh seeded RNG.
new_edgar_design        Constructor: build an 'edgar_design' S3 object.
print.edgar_design      'print' method for 'edgar_design'. Mirrors the
                        upstream CLI summary.
propose_alpha_structures
                        Propose viable alpha design structures for a
                        given treatment count. Mirrors the upstream
                        Python 'choose_design(treatment_count)' helper.
register_design         Register a design.
seeded_randint          Return a random integer between a and b
                        inclusive.
seeded_sample           Return k random items from 'items', without
                        replacement.
seeded_shuffle          Return a new vector with 'items' shuffled by
                        'rng'.
total_units             Total number of experimental units.
validate_design         Public dispatcher: validate design parameters
                        by key.
validate_treatment_count
                        Validate treatment counts. Mirrors
                        'validate_treatment_count'.
with_edgar_seed         Run a block of code with an isolated seeded
                        RNG, restoring the caller's '.Random.seed'
                        afterwards. This is the safety net for any
                        internal code that calls base R 'sample()' or
                        'runif()' directly; the 'make_rng()' /
                        'seeded_shuffle()' path does not touch the
                        global state, so this wrapper is provided for
                        completeness and for users who want to call
                        base R sampling under a known seed.
write_edgar_csv         Write a design to a CSV file.
write_edgar_json        Write a design to a JSON file (or return as a
                        string).
write_edgar_xlsx        Write a design to an XLSX file.
