Constructs D-optimal or near D-optimal nested and crossed
block designs for unstructured or general factorial treatment designs.
The treatment design, if required, is found from a model
matrix design formula and can be added sequentially, if required.
The block design is found from a defined
set of block factors and is conditional on the defined treatment design.
The block factors are added in sequence and each added block factor
is optimized conditional on all previously added block factors.
The block design can have repeated nesting down to any required
depth of nesting with either simple nested blocks or a
crossed blocks design at each level of nesting. Outputs include a table
showing the allocation of treatments to blocks and tables showing
the achieved D-efficiency factors for each block and treatment design.

Constructs D-optimal or near D-optimal nested and crossed
block designs for unstructured or general factorial treatment designs.
The treatment design, if required, is found from a model
matrix design formula and can be added sequentially, if required.
The block design is found from a defined
set of block factors and is conditional on the defined treatment design.
The block factors are added in sequence and each added block factor
is optimized conditional on all previously added block factors.
The block design can have repeated nesting down to any required
depth of nesting with either simple nested blocks or
crossed blocks at each level of nesting. Outputs include a table
showing the allocation of treatments to blocks and tables showing
the achieved D-efficiency factors for each block and treatment design.

Nested and Crossed Block Designs for Factorial and Unstructured
Treatment Sets
Constructs D-optimal or near D-optimal treatment and block designs for linear
treatment models with crossed or nested block factors. The treatment design can be any
arbitrary linear model defined by a treatment model formula and the block design can be
any feasible combination of crossed or nested block factors. The block design factors
are optimized sequentially and the levels of each successive block factor are optimized
within the levels of each preceding block factor. Crossed block designs with non-singular
interaction effects are optimized using a weighting scheme that allows for differential
weighting of first and second-order block effects. Outputs include a table showing the
allocation of treatments to blocks and tables showing the achieved D-efficiency factors
for each block and treatment design.