This tool performs pairwise correlation analysis and estimate causality.
    Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria.

While data from randomized experiments remain the gold standard for causal inference, estimation of causal estimands from observational data is possible through various confounding adjustment methods. However, the challenge of unmeasured confounding remains a concern in causal inference, where failure to account for unmeasured confounders can lead to biased estimates of causal estimands. Sensitivity analysis within the framework of causal inference can help adjust for possible unmeasured confounding. In `causens`, three main methods are implemented: adjustment via sensitivity functions (Brumback, Hernán, Haneuse, and Robins (2004) <doi:10.1002/sim.1657> and Li, Shen, Wu, and Li (2011) <doi:10.1093/aje/kwr096>), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) <doi:10.1002/sim.7298>).

Design and use of control charts for detecting mean changes 
 based on a delayed updating of the in-control parameter estimates.
 See Capizzi and Masarotto (2019) <doi:10.1080/00224065.2019.1640096> for the
 description of the method.

Provides six variants of two-way correspondence analysis (ca):
   simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca,
   singly ordered non symmetrical ca, and doubly ordered non symmetrical
   ca.

Cluster analysis is performed using pairwise distance information and a random partition distribution. The method is
             implemented for two random partition distributions. It draws samples and then obtains and plots clustering estimates.
             An implementation of a selection algorithm is provided for the mass parameter of the partition distribution. Since 
             pairwise distances are the principal input to this procedure, it is most comparable to the hierarchical and k-medoids
             clustering methods. The method is Dahl, Andros, Carter (2022+) <doi:10.1002/sam.11602>.