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-4.6-x86_64)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:

openblas cpp openmp

2.00 score 1 stars 199 downloads 3 exports 11 dependencies

Last updated from:f293dfb4a1. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	173
linux-devel-x86_64	OK	184
source / vignettes	OK	194
linux-release-arm64	OK	134
linux-release-x86_64	OK	160
macos-release-arm64	OK	105
macos-release-x86_64	OK	239
macos-oldrel-arm64	OK	89
macos-oldrel-x86_64	OK	267
windows-devel-x86_64	OK	140
windows-release-x86_64	OK	133
windows-oldrel-x86_64	OK	117
wasm-release	OK	107