Authors@R: c(person("Evgenia", "Dimitriadou", role = "aut"),
person("Kurt", "Hornik", role = c("ctb", "cre"), email =
Description: Convex Clustering methods, including K-means algorithm,
Gas algorithm (Soft Competitive Learning), and calculation of
Author: Evgenia Dimitriadou [aut], Kurt Hornik [ctb, cre]

On-line Update algorithm (Hard Competitive Learning) and Neural Gas
algorithm (Soft Competitive Learning), and calculation of several
indexes for finding the number of clusters in a data set.

Title: Comparative Causal Mediation Estimation
Authors@R: c(person("Kirk", "Bansak", role = "aut",
person("Xiaohan", "Wu", role = c("aut", "cre"),
Description: 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>.
Packaged: 2021-09-14 16:45:31 UTC; wuxiaohan

Title: Wraps 'CCM' with Utility Functions
Description: Provides a set of functions to perform queries against the 'CCM' API <https://mohcontacttracing.my.salesforce.com>.

Title: Consensus Clustering for Different Sample Coverage Data
person(given = "Chuanxing", family = "Li", email = "chuan-xing.li@ki.se", role = c("aut", "cre")),
person(given = "Meng", family = "Zhou", email="zhoumeng@wmu.edu.cn", role="aut"))
Description: 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).
Imports: ggplot2, diceR, parallel, tidyr, SNFtool, plyr,