of Statistical Software <doi:10.18637/jss.v090.i13> (included as
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

Description: Flexible univariate count models based on renewal processes. The
models may include covariates and can be specified with familiar formula
syntax as in glm() and package 'flexsurv'.  The methodology is described by
Kharrat et all (2019) <doi:10.18637/jss.v090.i13> (included as vignette

Title: Mean and variance modeling of count data
Description: Modeling under- and over-dispersed count data using

Description: Modeling under- and over-dispersed count data using extended Poisson process models (EPPM)

Title: Mean and Variance Modeling of Count Data
Description: Modeling under- and over-dispersed count data using extended Poisson process models (EPPM).

Description: Modeling under- and over-dispersed count data using extended Poisson process models (EPPM) as in the article Faddy and Smith (2011) <doi:10.18637/jss.v069.i06> .

Title: Combine Partial Covariance / Relationship Matrices
Author: Deniz Akdemir, Mohamed Somo, Julio Isidro Sanchez
Description: Combine partial covariance matrices using a Wishart-EM algorithm.
Methods are described in the November 2019 article by Akdemir et al.
It can be used to combine partially overlapping covariance matrices
from independent trials, partially overlapping multi-view relationship
data from genomic experiments, partially overlapping Gaussian graphs
described by their covariance structures.
high dimensional covariance graph estimation.
Suggests: knitr, plyr, spcov, qgraph, igraph
Packaged: 2020-01-13 21:57:38 UTC; denizakdemir

Title: Covariance Regression with Random Forests
c(person(given = "Cansu", family = "Alakus", role = c("aut", "cre"), email = "cansu.alakus@hec.ca"),
person(given = "Denis", family = "Larocque", role = c("aut"), email = "denis.larocque@hec.ca"),
person(given = "Aurelie", family = "Labbe", role = c("aut"), email = "aurelie.labbe@hec.ca"),
person(given = "Hemant", family = "Ishwaran", role = c("ctb"), comment = "Author of included 'randomForestSRC' codes"),
person(given = "Udaya B.", family = "Kogalur", role = c("ctb"), comment = "Author of included 'randomForestSRC' codes"))
Description: Covariance Regression with Random Forests ('CovRegRF') is a
random forest method for estimating the covariance matrix of a
multivariate response given a set of covariates. Random forest trees
are built with a new splitting rule which is designed to maximize the
distance between the sample covariance matrix estimates of the child
nodes. The method is described in Alakus et al. (2022)
<arXiv:2209.08173>. 'CovRegRF' uses 'randomForestSRC' package
<https://cran.r-project.org/package=randomForestSRC> by freezing at the
version 3.1.0. The custom splitting rule feature is utilised to apply the
Packaged: 2022-09-21 14:55:43 UTC; cansualakus
Hemant Ishwaran [ctb] (Author of included 'randomForestSRC' codes),
Udaya B. Kogalur [ctb] (Author of included 'randomForestSRC' codes)