Package: RMFM 1.1.0

RMFM: Robust Matrix Factor Model

We introduce a robust matrix factor model that explicitly incorporates tail behavior and employs a mean-shift term to avoid efficiency losses through pre-centering of observed matrices. More details on the methods related to our paper are currently under submission. A full reference to the paper will be provided in future versions once the paper is published.

Authors:Wei Liu [aut, cre], Xiaolu Jiang [aut], Jinyu Nie [aut]

RMFM_1.1.0.tar.gz
RMFM_1.1.0.zip(r-4.7-x86_64)RMFM_1.1.0.zip(r-4.7-arm64)RMFM_1.1.0.zip(r-4.6-x86_64)RMFM_1.1.0.zip(r-4.6-arm64)RMFM_1.1.0.zip(r-4.5-x86_64)
RMFM_1.1.0.tgz(r-4.6-x86_64)RMFM_1.1.0.tgz(r-4.6-arm64)RMFM_1.1.0.tgz(r-4.5-x86_64)RMFM_1.1.0.tgz(r-4.5-arm64)
RMFM_1.1.0.tar.gz(r-4.7-arm64)RMFM_1.1.0.tar.gz(r-4.7-x86_64)RMFM_1.1.0.tar.gz(r-4.6-arm64)RMFM_1.1.0.tar.gz(r-4.6-x86_64)
RMFM_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
RMFM/json (API)

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

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

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

On CRAN:

Conda:

openblascppopenmp

2.00 score 1 stars 253 downloads 3 exports 11 dependencies

Last updated from:f293dfb4a1. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK156
linux-devel-x86_64OK138
source / vignettesOK377
linux-release-arm64OK101
linux-release-x86_64OK136
macos-release-arm64OK142
macos-release-x86_64OK245
macos-oldrel-arm64OK198
macos-oldrel-x86_64OK294
windows-devel-arm64OK207
windows-devel-x86_64OK164
windows-release-arm64OK253
windows-release-x86_64OK127
windows-oldrel-x86_64OK117
wasm-releaseOK92

Exports:ER.RMFMgendata_rmfmRMFM

Dependencies:CholWishartCOAPglueirlbaLaplacesDemonlatticeMASSMatrixMixMatrixRcppRcppArmadillo