Provides fast dynamic-programming algorithms in 'C++'/'Rcpp'
    (with pure 'R' fallbacks) for the exact finite-sample distributions
    and p-values of Christoffersen (1998) independence (IND) and
    conditional-coverage (CC) VaR backtests. For completeness, it also
    provides the exact unconditional-coverage (UC) test following
    Kupiec (1995) via a closed-form binomial enumeration. See
    Christoffersen (1998) <doi:10.2307/2527341> and Kupiec (1995)
    <doi:10.3905/jod.1995.407942>.

Implements comprehensive test data engineering methods as described in 
    Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for 
    engineering and processing test data: Classical Test Theory (CTT) with 
    reliability coefficients for continuous ability assessment; Item Response 
    Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information 
    functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank 
    Analysis (LRA) for ordinal clustering with automatic determination of cluster 
    numbers; Biclustering methods including infinite relational models for 
    simultaneous clustering of examinees and items without predefined cluster 
    numbers; and Bayesian Network Models (BNM) for visualizing inter-item 
    dependencies. Features local dependence analysis through LRA and biclustering, 
    parameter estimation, dimensionality assessment, and network structure 
    visualization for educational, psychological, and social science research.

A 'Shiny'-based toolkit for item/test analysis. It is designed for multiple-choice, true-false, and open-ended questions. The toolkit is usable with datasets in 1-0 or other formats. Key analyses include difficulty, discrimination, response-option analysis, reports. The classical test theory methods used are described in Ebel & Frisbie (1991, ISBN:978-0132892314).

Contains all data sets for Exam PA: Predictive Analytics at 
    <https://exampa.net/>.

Take the examples written in your documentation of functions
    and use them to create shells (skeletons which must be manually
    completed by the user) of test files to be tested with the 'testthat'
    package. Sort of like python 'doctests' for R.