Joint N-Mixture Models for Site-Associated Species
c(person("Rafael", "de Andrade Moral", role = c("aut", "cre"), email = "rafael_moral@yahoo.com.br"), person("Clarice", "Garcia Borges Demetrio", role = "aut"), person("John", "Hinde", role = "aut"))
Rafael de Andrade Moral [aut, cre], Clarice Garcia Borges Demetrio [aut], John Hinde [aut]
Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.

Risk estimation using the joint probability method
Feifei Zheng, Michael Leonard, Seth Westra
A bivariate integration method to estimate risk caused by two extreme and dependent forcing variables.

Joint Segmentation of Multivariate (Copy Number) Signals
c(person("Morgane", "Pierre-Jean", role = c("aut", "cre"),
email = "mpierrejean.pro@gmail.com"),
person("Pierre", "Neuvial", role = c("aut"),
email="pierre.neuvial@math.univ-toulouse.fr"),
person("Guillem", "Rigaill", role = "aut"))
Methods for fast segmentation of multivariate
signals into piecewise constant profiles and for generating realistic
copy-number profiles. A typical application is the joint segmentation of total
DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism
(SNP) microarrays in cancer studies. The methods are described in Pierre-Jean,
Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>.
acnr (>= 0.3.1), matrixStats (>= 0.6.0), DNAcopy
PSCBS, R.cache, digest, changepoint (>= 1.0.2), knitr,
rmarkdown, testthat
Morgane Pierre-Jean [aut, cre],
Pierre Neuvial [aut],
Guillem Rigaill [aut]

A package for Multilevel Joint Modelling Multiple Imputation
Building on Schafer's pan and on standalone program REALCOM, jomo is a package for multilevel joint modelling multiple imputation.
Binary and categorical variables are handled through latent normal variables and algorithms for cluster specific covariance matrices are introduced.

Multilevel Joint Modelling Multiple Imputation
Building on Schafer's package pan and on the standalone program REALCOM, jomo is a package for multilevel joint modelling multiple imputation.
Binary and categorical variables are handled through latent normal variables and algorithms for cluster-specific covariance matrices are introduced.