Latent and Stochastic Block Model estimation by a Variational EM algorithm.
             Various probability distribution are provided (Bernoulli,
             Poisson...), with or without covariates.

Create randomizations for block random clinical trials.  Can also produce a pdf file of randomization cards.

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.

Functions to combine data on voting blocs' 
    size, turnout, and vote choice to estimate each bloc's
    vote contributions to the Democratic and Republican parties.
    The package also includes functions for uncertainty estimation and
    plotting. Users may define voting blocs along a discrete or continuous variable.
    The package implements methods described in Grimmer, Marble, and Tanigawa-Lau (2023) <doi:10.31235/osf.io/c9fkg>.