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>.

Provides visualization tools for sensitivity analysis to
    unmeasured confounding in observational studies. Includes contour-based
    sensitivity plots, robustness curves, and benchmark-oriented graphics
    that help researchers assess how strong omitted confounding would need
    to be to attenuate, invalidate, or reverse estimated effects. Supports
    regression-based sensitivity analysis frameworks, including impact
    threshold approaches (Frank, 2000,
    <doi:10.1177/0049124100029002001>), partial R-squared methods
    (Cinelli and Hazlett, 2020, <doi:10.1111/rssb.12348>), and E-value
    style metrics (VanderWeele and Ding, 2017,
    <doi:10.7326/M16-2607>). Emphasizes clear, interpretable, and
    publication-ready graphical summaries for transparent reporting of
    causal sensitivity analyses across the social, behavioral, health,
    and educational sciences.

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.

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.