Provide a variety of Q-matrix validation methods for the generalized
    cognitive diagnosis models, including the method based on the generalized
    deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011)
    <DOI:10.1007/s11336-011-9207-7> discrimination index (the GDI method) by
    de la Torre and Chiu (2016) <DOI:10.1007/s11336-015-9467-8>, the Hull method
    by Najera et al. (2021) <DOI:10.1111/bmsp.12228>, the stepwise Wald test method
    (the Wald method) by Ma and de la Torre (2020) <DOI:10.1111/bmsp.12156>, the
    multiple logistic regression‑based Q‑matrix validation method (the MLR-B method)
    by Tu et al. (2022) <DOI:10.3758/s13428-022-01880-x>, the beta method based on
    signal detection theory by Li and Chen (2024) <DOI:10.1111/bmsp.12371> and
    Q-matrix validation based on relative fit index by Chen et al. (2013)
    <DOI:10.1111/j.1745-3984.2012.00185.x>. Different research methods and iterative
    procedures during Q-matrix validating are available
    <DOI:10.3758/s13428-024-02547-5>.

Finding hidden clusters in structured data can be hindered
  by the presence of masking variables. If not detected,
  masking variables are used to calculate the overall similarities between units, 
  and therefore the cluster attribution is more imprecise.
  The algorithm q-vars implements an optimization method to find the variables
  that most separate units between clusters. In this way, masking variables can be 
  discarded from the data frame and the clustering is more accurate.
  Tests can be found in Benati et al.(2017) <doi:10.1080/01605682.2017.1398206>.

Functions to compute quasi variances and associated measures of approximation error.

Pure-R parser for the binary '.Q-View' project file format
    used in chemiluminescent multiplex ELISA plate imaging and
    quantification. Reads the embedded H2 database container and CSV
    report, and returns project metadata, the analyte panel with units
    and detection limits, sample well-group assignments, per-well
    pixel-intensity replicates, summary statistics, optional
    back-calculated concentrations, and a plate layout, all as tidy
    tibbles. No Java runtime or H2 database driver is required.