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 in a forthcoming paper in the Journal
of Statistical Software <10.18637/jss.v090.i13> (included as
vignette 'Countr_guide' in the package).
Matrix, Rcpp (>= 0.11.3), flexsurv, Formula, VGAM, optimx,
numDeriv, boot, MASS, car, utils, Rdpack (>= 0.7-0), lattice,
RColorBrewer, dplyr, standardize, pscl, lmtest, xtable

c( person(given = "Tarak", family = "Kharrat",
role = c("aut"),
email = "tarak@realanalytics.co.uk"),
person(given = c("Georgi", "N."), family = "Boshnakov",
role = c("aut", "cre"),
email = "georgi.boshnakov@manchester.ac.uk") )
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 in a forthcoming paper in the Journal
of Statistical Software <doi:10.18637/jss.v090.i13> (included as
vignette 'Countr_guide' in the package).
Tarak Kharrat [aut],
Georgi N. Boshnakov [aut, cre]

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
'Countr_guide' in the package).

Mean and variance modeling of count data
Modeling under- and over-dispersed count data using
extended Poisson process models (EPPM)

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

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

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> .

Combine Partial Covariance / Relationship Matrices
Deniz Akdemir, Mohamed Somo, Julio Isidro Sanchez
Combine partial covariance matrices using a Wishart-EM algorithm.
Methods are described in the November 2019 article by Akdemir et al.
<https://www.biorxiv.org/content/10.1101/857425v1>.
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 estimation,
multi-view data integration.
high dimensional covariance graph estimation.