person("Cinzia", "Viroli", email = "cinzia.viroli@unibo.it", role = "ctb", comment = c(ORCID = "0000-0002-3278-5266")),
person("Isobel Claire", "Gormley", email = "claire.gormley@ucd.ie", role = "ctb", comment = c(ORCID = "0000-0001-7713-681X")))
Isobel Claire Gormley [ctb] (<https://orcid.org/0000-0001-7713-681X>)

Title: Increamental Mixture Importance Sampling
Description: IMIS algorithm draws samples from the posterior
distribution. The user has to define the following R functions
in advance: prior(x) calculates prior density of x,
likelihood(x) calculates the likelihood of x, and
sample.prior(n) draws n samples from the prior distribution.
Packaged: Tue Sep 7 14:18:47 2010; Le Bao

Title: Gaussian Mixture Model for Multi-Omics Data Integration
Authors@R: c(person("Ziqiao","Wang",role=c("aut","cre"),email="wzqjanet@gmail.com",comment = c(ORCID = "0000-0003-3383-8670")),person("Peng","Wei",role=c("ths"),email="pwei2@mdanderson.org",comment = c(ORCID = "0000-0001-7758-6116")))
Description: A multivariate Gaussian mixture model framework to integrate multiple types of genomic data and allow modeling of inter-data-type correlations for association analysis. 'IMIX' can be implemented to test whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation, gene expression, etc. It can also study the integration of multiple pathways. 'IMIX' uses the summary statistics of association test outputs and conduct integration analysis for two or three types of genomics data. 'IMIX' features statistically-principled model selection, global FDR control and computational efficiency. Details are described in Ziqiao Wang and Peng Wei (2020) <doi:10.1093/bioinformatics/btaa1001>.
Author: Ziqiao Wang [aut, cre] (<https://orcid.org/0000-0003-3383-8670>),

Title: Interactive Model Performance Evaluation
Authors@R: person("Anup", "Nair", email = "nairanup50695@gmail.com", role = c("aut","cre"))
Description: Contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).
Imports: dplyr, ggplot2, stats, tidyr, shiny