Performing multiple-class cluster correspondence analysis(MCCCA). The main functions are create.MCCCAdata() to create a list to be applied to MCCCA, MCCCA() to apply MCCCA, and plot.mccca() for visualizing MCCCA result. Methods used in the 
  package refers to Mariko Takagishi and Michel van de Velden (2022)<doi:10.1080/10618600.2022.2035737>.

The MCC-F1 analysis is a method to evaluate the performance of binary classifications. 
    The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth.
    The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification.

Implements methods for processing a sample of (hard)
        clusterings, e.g. the MCMC output of a Bayesian clustering
        model. Among them are methods that find a single best
        clustering to represent the sample, which are based on the
        posterior similarity matrix or a relabelling algorithm.

The IRLS (Iteratively Reweighted Least Squares) and GMM (Generalized Method of Moments) methods are applied to estimate mixed correlation coefficient matrix (Pearson, Polyseries, Polychoric), which can be estimated in pairs or simultaneously. For more information see Peng Zhang and Ben Liu (2024) <doi:10.1080/10618600.2023.2257251>; Ben Liu and Peng Zhang (2024) <doi:10.48550/arXiv.2404.06781>.

Applying the methodology from Manuel et al. to estimate parameters using a matched 
    case control data with a mismeasured exposure variable that is accompanied by 
    instrumental variables (Submitted).

Calculates mean cumulative count (MCC) to estimate the expected cumulative number of recurrent events per person over time in the presence of competing risks and censoring. Implements both the Dong-Yasui equation method and sum of cumulative incidence method described in Dong, et al. (2015) <doi:10.1093/aje/kwu289>. Supports inverse probability weighting for causal inference as outlined in Gaber, et al. (2023) <doi:10.1093/aje/kwad031>. Provides S3 methods for printing, summarizing, plotting, and extracting results. Handles grouped analyses and integrates with 'ggplot2' <https://ggplot2.tidyverse.org/> for visualization.