MultiCOAP: High-Dimensional Covariate-Augmented Overdispersed Multi-Study Poisson Factor Model

We introduce factor models designed to jointly analyze high-dimensional count data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among counts with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors and the rank of regression coefficient matrix. More details can be referred to Liu et al. (2024) <doi:10.48550/arXiv.2402.15071>.

Version:	1.1
Depends:	irlba, R (≥ 3.5.0)
Imports:	MASS, Rcpp (≥ 1.0.10)
LinkingTo:	Rcpp, RcppArmadillo
Published:	2024-03-07
DOI:	10.32614/CRAN.package.MultiCOAP
Author:	Wei Liu [aut, cre], Qingzhi Zhong [aut]
Maintainer:	Wei Liu <liuweideng at gmail.com>
BugReports:	https://github.com/feiyoung/MultiCOAP/issues
License:	GPL-3
URL:	https://github.com/feiyoung/MultiCOAP
NeedsCompilation:	yes
CRAN checks:	MultiCOAP results

Documentation:

Reference manual:

MultiCOAP.html , MultiCOAP.pdf

Downloads:

Package source:	MultiCOAP_1.1.tar.gz
Windows binaries:	r-devel: MultiCOAP_1.1.zip, r-release: MultiCOAP_1.1.zip, r-oldrel: MultiCOAP_1.1.zip
macOS binaries:	r-release (arm64): MultiCOAP_1.1.tgz, r-oldrel (arm64): MultiCOAP_1.1.tgz, r-release (x86_64): MultiCOAP_1.1.tgz, r-oldrel (x86_64): MultiCOAP_1.1.tgz

Reverse dependencies:

Reverse imports:

MMGFM

Linking:

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