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.

The stepwise variable selection procedure (with iterations
 between the 'forward' and 'backward' steps) can be used to obtain
 the best candidate final regression model in regression analysis.
 All the relevant covariates are put on the 'variable list' to be
 selected. The significance levels for entry (SLE) and for stay
 (SLS) are usually set to 0.15 (or larger) for being conservative.
 Then, with the aid of substantive knowledge, the best candidate
 final regression model is identified manually by dropping the
 covariates with p value > 0.05 one at a time until all regression
 coefficients are significantly different from 0 at the chosen alpha
 level of 0.05.

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).

The chi-squared test for goodness of fit 
    and an independence test. In addition, the chi-squared test for the population variance,
    the function for computing a confidence interval for the population variance, 
    and the function for calculating the population standard deviation are provided.