An implementation of efficiency first conformal prediction (EFCP) and validity first conformal prediction (VFCP) that demonstrates both validity (coverage guarantee) and efficiency (width guarantee). To learn how to use it, check the vignettes for a quick tutorial. The package is based on the work by Yang Y., Kuchibhotla A.,(2021) <arxiv:2104.13871>.

Offers several functions for Configural Frequencies
    Analysis (CFA), which is a useful statistical tool for the analysis of
    multiway contingency tables. CFA was introduced by G. A. Lienert as
    'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). 
    Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. 
    Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.

For multiple testing.
    Computes estimates and confidence bounds for the
    False Discovery Proportion (FDP), the fraction of false positives among
    all rejected hypotheses.
    The methods in the package use permutations of the data. Doing so, they
    take into account the dependence structure in the data.

Takes the outputs of a 'caret' confusion matrix and allows for the quick conversion of these list items to lists.
    The intended usage is to allow the tool to work with the outputs of machine learning classification models. 
    This tool works with classification problems for binary and multi-classification problems and allows for the record level conversion of the confusion matrix outputs.
    This is useful, as it allows quick conversion of these objects for storage in database systems and to track ML model performance over time.
    Traditionally, this approach has been used for highlighting model representation and feature slippage.

Narrow down the number of models to look at in model selection using the confidence envelopes based on the minimum ZIC (Generalized Information Criteria) values for regression and time series data. Functions involve the computation of multivariate normal-probabilities with covariance matrices based on minimum ZIC inverting the CDF of the minimum ZIC. It involves both the computation of singular and non-singular probabilities as described in Genz (1992) <[https:doi.org/10.2307/1390838]https:doi.org/10.2307/1390838>.