Package: tcv 0.1.0

Zhijing Wang

tcv: Determining the Number of Factors in Poisson Factor Models via Thinning Cross-Validation

Implements methods for selecting the number of factors in Poisson factor models, with a primary focus on Thinning Cross-Validation (TCV). The TCV method is based on the 'data thinning' technique, which probabilistically partitions each count observation into training and test sets while preserving the underlying factor structure. The Poisson factor model is then fit on the training set, and model selection is performed by comparing predictive performance on the test set. This toolkit is designed for researchers working with high-dimensional count data in fields such as genomics, text mining, and social sciences. The data thinning methodology is detailed in Dharamshi et al. (2025) <doi:10.1080/01621459.2024.2353948> and Wang et al. (2025) <doi:10.1080/01621459.2025.2546577>.

Authors:Zhijing Wang [aut, cre], Heng Peng [aut], Peirong Xu [aut]

tcv_0.1.0.tar.gz
tcv_0.1.0.tar.gz(r-4.7-arm64)tcv_0.1.0.tar.gz(r-4.7-x86_64)tcv_0.1.0.tar.gz(r-4.6-arm64)tcv_0.1.0.tar.gz(r-4.6-x86_64)
tcv_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
tcv/json (API)

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

Bug tracker:https://github.com/wangzhijingwzj/tcv/issues

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

On CRAN:

Conda:

openblascppopenmp

1.00 score 177 downloads 3 exports 13 dependencies

Last updated from:cc9e2cc362. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK119
linux-devel-x86_64OK127
source / vignettesOK184
linux-release-arm64OK119
linux-release-x86_64OK123
wasm-releaseOK121

Exports:add_identifiabilitychooseFacNumber_ratiomultiDT

Dependencies:codetoolscountsplitdoSNOWforeachGFMirlbaiteratorslatticeMASSMatrixRcppRcppArmadillosnow