c(person("Keefe", "Murphy", email = "keefe.murphy@mu.ie", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7709-3159")),
person("Cinzia", "Viroli", email = "cinzia.viroli@unibo.it", role = "ctb"),
person("Isobel Claire", "Gormley", email = "claire.gormley@ucd.ie", role = "ctb"))
Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.
matrixStats (>= 0.53.1), mclust (>= 5.4), mvnfast, Rfast (>=
1.9.8), slam, viridisLite
gmp (>= 0.5-4), knitr, mcclust, rmarkdown, Rmpfr

c(person("Keefe", "Murphy", email = "keefe.murphy@mu.ie", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7709-3159")),
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")))
Keefe Murphy [aut, cre] (<https://orcid.org/0000-0002-7709-3159>),
Cinzia Viroli [ctb] (<https://orcid.org/0000-0002-3278-5266>),
Isobel Claire Gormley [ctb] (<https://orcid.org/0000-0001-7713-681X>)

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.

Gaussian Mixture Model for Multi-Omics Data Integration
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")))
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>.