Generates efficient balanced mixed-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has EfNOD efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient mixed-level k-circulant design through a progress bar. The progress of 100 per cent means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs. For more details, please see Mandal, B.N., Gupta V. K. and Parsad, R. (2011). Construction of Efficient Mixed-Level k-Circulant Supersaturated Designs, Journal of Statistical Theory and Practice, 5:4, 627-648, <doi:10.1080/15598608.2011.10483735>.

Provides a convenient interface in 'OpenMx' for building
    Estabrook's (2015) <doi:10.1037/a0034523> Measurement Model of
    Derivatives (MMOD).

Implements methods to normalize multiplexed imaging data, including
    statistical metrics and visualizations to quantify technical variation in 
    this data type. Reference for methods listed here: Harris, C., Wrobel, J., 
    & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging 
    Data. Journal of Open Source Software, 7(71), 4180, 
    <doi:10.21105/joss.04180>.

Provides a 'lavaan'-like syntax for 'OpenMx' models. The syntax supports 
  definition variables, bounds, and parameter transformations. This allows for
  latent growth curve models with person-specific measurement occasions, moderated
  nonlinear factor analysis and much more.

Implementation of adaptive assessment procedures based
    on Knowledge Space Theory (KST, Doignon & Falmagne, 1999 <ISBN:9783540645016>) and Formal Psychological Assessment
    (FPA, Spoto, Stefanutti & Vidotto, 2010 <doi:10.3758/BRM.42.1.342>) frameworks. An adaptive assessment is a type of evaluation that
    adjusts the difficulty and nature of subsequent questions based on the
    test taker's responses to previous ones. The package contains functions
    to perform and simulate an adaptive assessment. Moreover, it is
    integrated with two 'Shiny' interfaces, making it both accessible and
    user-friendly.  The package has been partially funded by the European Union -
    NextGenerationEU and by the Ministry of University and Research (MUR),
    National Recovery and Resilience Plan (NRRP), Mission 4, Component 2,
    Investment 1.5, project “RAISE - Robotics and AI for Socio-economic
    Empowerment” (ECS00000035).