Package: MultiCOAP 1.1

Wei Liu

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>.

Authors:Wei Liu [aut, cre], Qingzhi Zhong [aut]

MultiCOAP_1.1.tar.gz
MultiCOAP_1.1.zip(r-4.7-x86_64)MultiCOAP_1.1.zip(r-4.7-arm64)MultiCOAP_1.1.zip(r-4.6-x86_64)MultiCOAP_1.1.zip(r-4.6-arm64)
MultiCOAP_1.1.tar.gz(r-4.7-arm64)MultiCOAP_1.1.tar.gz(r-4.7-x86_64)MultiCOAP_1.1.tar.gz(r-4.6-arm64)MultiCOAP_1.1.tar.gz(r-4.6-x86_64)
MultiCOAP_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MultiCOAP/json (API)

# Install 'MultiCOAP' in R:
install.packages('MultiCOAP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/feiyoung/multicoap/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

1.48 score 1 packages 222 downloads 3 exports 6 dependencies

Last updated from:cbb361d9e7. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK111
linux-devel-x86_64OK979
source / vignettesOK593
linux-release-arm64OK112
linux-release-x86_64OK133
windows-devel-arm64OK256
windows-devel-x86_64OK168
windows-release-arm64OK221
windows-release-x86_64OK138
wasm-releaseOK294

Exports:gendata_simu_multi2MSFRVIMultiCOAP

Dependencies:irlbalatticeMASSMatrixRcppRcppArmadillo