Constructs treatment and block designs for linear treatment models
  with crossed or nested block factors. The treatment design can be any feasible 
  linear model and the block design can be any feasible combination of crossed or 
  nested block factors. The block design is a sum of one or more block factors
  and the block design is optimized sequentially with the levels of each successive
  block factor optimized conditional on all previously optimized block factors. 
  D-optimality is used throughout except for square or rectangular lattice block designs 
  which are constructed algebraically using mutually orthogonal Latin squares.
  Crossed block designs with interaction effects are optimized using a weighting scheme
  which 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.  
  Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. 
  JABES 25, 500–522 (2020) <doi:10.1007/s13253-020-00416-0>.

Blocks units into experimental blocks, with one unit per treatment condition, by creating a measure of multivariate distance between all possible pairs of units. Maximum, minimum, or an allowable range of differences between units on one variable can be set. Randomly assign units to treatment conditions. Diagnose potential interference between units assigned to different treatment conditions.  Write outputs to .tex and .csv files. For more information on the methods implemented, see Moore (2012) <doi:10.1093/pan/mps025>.

Implements the Block-wise Rank in Similarity Graph Edge-count 
    test (BRISE), a rank-based two-sample test designed for block-wise 
    missing data. The method constructs (pattern) pair-wise similarity graphs 
    and derives quadratic test statistics with asymptotic chi-square 
    distribution or permutation-based p-values. It provides both 
    vectorized and congregated versions for flexible inference. 
    The methodology is described in Zhang, Liang, Maile, and 
    Zhou (2025) <doi:10.48550/arXiv.2508.17411>.