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
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RMFM.pdf |RMFM.html✨
RMFM/json (API)

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

Peer review:

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:

openblascppopenmp

2.00 score 3 exports 11 dependencies

Last updated 1 months agofrom:f293dfb4a1. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 27 2024
R-4.5-win-x86_64OKNov 27 2024
R-4.5-linux-x86_64OKNov 27 2024
R-4.4-win-x86_64OKNov 27 2024
R-4.4-mac-x86_64OKNov 27 2024
R-4.4-mac-aarch64OKNov 27 2024
R-4.3-win-x86_64OKNov 27 2024
R-4.3-mac-x86_64OKNov 27 2024
R-4.3-mac-aarch64OKNov 27 2024

Exports:ER.RMFMgendata_rmfmRMFM

Dependencies:CholWishartCOAPglueirlbaLaplacesDemonlatticeMASSMatrixMixMatrixRcppRcppArmadillo