This function implements the MixMAP algorithm, which performs gene
level tests of association using data from a previous GWAS or data from a
meta-analysis of several GWAS.  Conceptually, genes are detected as
significant if the collection of p-values within a gene are determined to
be collectively smaller than would be observed by chance.

'mixmapTest.R' 'mixmapPI.R' 'MixMAPclass.R' 'plot.MixMAP.R'
'summary.MixMAP.R'

A collection of functions to implement the MixMAP algorithm, which performs gene
level tests of association using data from a previous GWAS or data from a
meta-analysis of several GWAS.  Conceptually, genes are detected as
significant if the collection of p-values within a gene are determined to
be collectively smaller than would be observed by chance.

Classification with Matrix Variate Normal and t Distributions
c(person(c("Geoffrey", "Thompson"),email="gzthompson@gmail.com",
role = c("aut", "cre"), comment = c(ORCID = '0000-0003-2436-8822')),
person(c("B. D.", "Ripley",
comment = "author of original lda and qda functions"),
email = "ripley@stats.ox.ac.uk", role = c("ctb","cph")),
person(c("W. N.", "Venables",
comment = "author of original lda and qda functions"),
role = c("ctb","cph")))
Provides sampling and density functions for matrix
variate normal, t, and inverted t distributions;  ML estimation for matrix
variate normal and t distributions using the EM algorithm,
including some restrictions on the parameters; and classification by linear and
quadratic discriminant analysis for matrix variate normal and t
distributions described in Thompson et al. (2019) <arXiv:1907.09565>.
Performs clustering with matrix variate normal and t mixture models.
knitr, rmarkdown, testthat, covr, ggplot2, dplyr, magrittr,
spelling
Geoffrey Thompson [aut, cre] (<https://orcid.org/0000-0003-2436-8822>),
B. D. Ripley author of original lda and qda functions [ctb, cph],
W. N. Venables author of original lda and qda functions [ctb, cph]

Provides sampling and density functions for matrix
variate normal, t, and inverted t distributions;  ML estimation for matrix
variate normal and t distributions using the EM algorithm,
including some restrictions on the parameters; and classification by linear and
quadratic discriminant analysis for matrix variate normal and t
distributions described in Thompson et al. (2019) <doi:10.1080/10618600.2019.1696208>.
Performs clustering with matrix variate normal and t mixture models.