Building on the docking layout manager provided by 'dockViewR',
    this provides a flexible front-end to 'blockr.core'. It provides an extension mechanism which allows for providing means to manipulate a board
    object via panel-based user interface components.

Extends 'blockr.core' with interactive blocks for visual data
    wrangling using 'dplyr' and 'tidyr' operations. Users can build data
    transformation pipelines through a graphical interface without writing code
    directly. Includes blocks for filtering, selecting, mutating, summarizing,
    joining, and arranging data, with support for complex expressions, grouping
    operations, and real-time validation.

Extends 'blockr.core' with interactive blocks for data
    visualization using 'ggplot2'. Users can build charts through a graphical
    interface without writing code directly. Includes common chart types
    (bar charts, line charts, pie charts, scatter plots) as well as statistical
    plots (boxplots, histograms, density plots, violin plots) with
    rich customization options and intuitive user interfaces.

Extends 'blockr.core' with interactive blocks for reading and
    writing data files. Supports CSV, Excel, Parquet, RDS, and other formats
    through a graphical interface without writing code directly. Includes
    file browser integration and configurable import/export options.

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>.