Functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020)<doi:10.1017/pan.2019.31>.

Consensus clustering, also called meta-clustering or cluster ensembles, has been increasingly 
    used in clinical data. Current consensus clustering methods tend to ensemble a number of different 
    clusters from mathematical replicates with similar sample coverage. As the fact of common variety
    of sample coverage in the real-world data, a new consensus clustering strategy dealing with
    such biological replicates is required. This is a two-step consensus clustering package, which
    is used to input multiple predictive labels with different sample coverage (missing labels).

Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision).

Implements the convex clustering through majorization-minimization (CCMM) algorithm described in Touw, Groenen, and Terada (2022) <arXiv:2211.01877> to perform minimization of the convex clustering loss function.

Provides a set of functions to perform queries against the 'CCM' API <https://mohcontacttracing.my.salesforce.com>.

Cases are matched to controls in an efficient, optimal and computationally flexible way. It uses 
             the idea of sub-sampling in the level of the case, by creating pseudo-observations of controls. The 
             user can select between replacement and without replacement, the number of controls, and several covariates 
             to match upon. See Mamouris (2021) <doi:10.1186/s12874-021-01256-3> for an overview.