Provides tools to generate HTML interfaces for adaptive
    and non-adaptive tests using the shiny
    package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). 
    Suitable for applying unidimensional and multidimensional
    computerized adaptive tests (CAT) using item response theory methodology and for
    creating simple questionnaires forms to collect response data directly in R.
    Additionally, optimal test designs (e.g., "shadow testing") are supported
    for tests that contain a large number of item selection constraints.
    Finally, package contains tools useful for performing Monte Carlo simulations 
    for studying test item banks.

Package for combined miRNA- and mRNA-testing.

Provides constrained joint maximum likelihood estimation
    algorithms for item factor analysis (IFA) based on multidimensional item response theory
    models. So far, we provide functions for exploratory and confirmatory IFA based on the 
    multidimensional two parameter logistic (M2PL) model for binary response data. Comparing 
    with traditional estimation methods for IFA, the methods implemented in this package scale
    better to data with large numbers of respondents, items, and latent factors. The computation
    is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to:
    1. Chen, Y., Li, X., & Zhang, S. (2018). Joint Maximum Likelihood Estimation for 
    High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 1-23. 
    <doi:10.1007/s11336-018-9646-5>;
    2. Chen, Y., Li, X., & Zhang, S. (2019). Structured Latent Factor Analysis for Large-scale Data: 
    Identifiability, Estimability, and Their Implications. Journal of the American Statistical 
    Association, <doi: 10.1080/01621459.2019.1635485>.

Provides singular value decomposition based estimation algorithms for exploratory item factor analysis (IFA) 
    based on multidimensional item response theory models. For more information, please refer to:
    Zhang, H., Chen, Y., & Li, X. (2020). A note on exploratory item factor analysis by 
    singular value decomposition. Psychometrika, 1-15, <DOI:10.1007/s11336-020-09704-7>.