Implements the Quantitative Classification-based on
    Association Rules (QCBA)  algorithm (<doi:10.1007/s10489-022-04370-x>). 
    QCBA postprocesses rule classification models making them typically smaller and in some cases more accurate. 
    Supported are 'CBA' implementations from 'rCBA', 'arulesCBA' and 'arc' packages, and 'CPAR', 'CMAR', 'FOIL2' and 'PRM' implementations 
    from 'arulesCBA' package and 'SBRL' implementation from the 'sbrl' package. The result of the post-processing is an ordered CBA-like rule list.

Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.

Functions to calculate Average Sample Numbers (ASN), Average Run Length (ARL1) and value of k, k1 and k2 for quality control charts under repetitive sampling as given in Aslam et al. (2014) (<DOI:10.7232/iems.2014.13.1.101>).

Tools for (automated and manual) quality control of 
  the results of Epigenome-Wide Association Studies.

Tools for (automated and manual) quality control of the results of Genome Wide Association Studies.

Provides functions for the joint analysis of Q sets of
    p-values obtained for the same list of items. This joint analysis is
    performed by querying a composite hypothesis, i.e. an arbitrary
    complex combination of simple hypotheses, as described in Mary-Huard
    et al. (2021) <doi:10.1093/bioinformatics/btab592> and De Walsche et
    al.(2023) <doi:10.1101/2024.03.17.585412>. In this approach, the
    Q-uplet of p-values associated with each item is distributed as a
    multivariate mixture, where each of the 2^Q components corresponds to
    a specific combination of simple hypotheses. The dependence between
    the p-value series is considered using a Gaussian copula function. A
    p-value for the composite hypothesis test is derived from the
    posterior probabilities.

Performs tuning of clustering models, methods and algorithms including the problem of determining an appropriate number of clusters. Validation of cluster analysis results is performed via quadratic scoring using resampling methods, as in Coraggio, L. and Coretto, P. (2023) <doi:10.1016/j.jmva.2023.105181>.