Performing Monte Carlo Expectation Maximization Random Forest
Imputation for Biological Data
person("Mohamed", "Soudy", email = "MohmedSoudy2009@gmail.com", role=c("aut", "cre"))
Perform missing value imputation for biological data using the random forest algorithm, the imputation aim to keep the original mean and standard deviation consistent after imputation.
missForest, ggpubr, progress, doParallel, foreach

A mixed collection of useful and semi-useful functions,
some of which may even be referenced in The R Primer book.

A mixed collection of useful and semi-useful functions, some of which may even be referenced in The R Primer book.

A mixed collection of useful and semi-useful functions, some of
which may even be referenced in The R Primer book.

MASS, Matrix, Rcpp, glmnet, kinship2, methods, mvtnorm,
parallel
A mixed collection of useful and semi-useful diverse
statistical functions, some of which may even be referenced in
The R Primer book.

person(given="Claus Thorn", family="Ekstrøm", role = c("aut", "cre"), email = "claus@rprimer.dk")
knitr, lme4, magrittr, rmarkdown, testthat

MASS, Matrix, Rcpp, geepack, geeM, ggplot2, ggformula, glmnet,
kinship2, methods, mvtnorm, parallel

Multivariate Exponentially Weighted Moving Average (MEWMA)
Control Chart
mewma package perform the Multivariate Exponentially
Weighted Moving Average (MEWMA) Control Chart

Fits the Mixture of Experts Latent Position Cluster Model to
Network Data
Fits the mixture of experts latent position cluster model to network data to cluster nodes into subgroups, while incorporating covariate information, in a mixture of experts model setting.
MASS, mvtnorm, mclust, nnet, ellipse, latentnet, vegan, e1071

Fitting the Mixture of Experts Latent Position Cluster Model to
Network Data
Isobel Claire Gormley and Thomas Brendan Murphy

Fit the Mixture of Experts Latent Position Cluster Model to
Network Data
c(person(given = "Isobel Claire",
family = "Gormley",
role = c("aut", "cre"),
email = "claire.gormley@ucd.ie"),
person(given = "Thomas Brendan",
family = "Murphy",
role = c("aut"),
email = "brendan.murphy@ucd.ie"))
Functions to facilitate model-based clustering of nodes in a network in a mixture of experts setting, which incorporates covariate information on the nodes in the modelling process. Isobel Claire Gormley and Thomas Brendan Murphy (2010) <doi:10.1016/j.stamet.2010.01.002>.
MASS, mvtnorm, nnet, ellipse, latentnet, vegan, e1071
Isobel Claire Gormley [aut, cre],
Thomas Brendan Murphy [aut]