Model selection method with multiple block-wise imputation for block-wise missing data; see Xue, F., and Qu, A. (2021) <doi:10.1080/01621459.2020.1751176>.

This is primarily meant as an implementation of generalized blockmodeling for valued networks.
 In addition, measures of similarity or dissimilarity based on structural equivalence and 
 regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted:
 Žiberna (2007)<doi:10.1016/j.socnet.2006.04.002>, Žiberna (2008)<doi:10.1080/00222500701790207>,
 Žiberna (2014)<doi:10.1016/j.socnet.2014.04.002>.

This app provides some useful tools for Offering an accessible GUI for generalised blockmodeling of single-relation, one-mode networks. The user can execute blockmodeling without having to write a line code by using the app's visual helps. Moreover, there are several ways to visualisations networks and their partitions. Finally, the results can be exported as if they were produced by writing code. The development of this package is financially supported by the Slovenian Research Agency (www.arrs.gov.si) within the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

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>.