Package: fnets 0.2.0

fnets: Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series

Implements methods for network estimation and forecasting of high-dimensional time series exhibiting strong serial and cross-sectional correlations under a factor-adjusted vector autoregressive model. See Barigozzi, Cho and Owens (2024) <doi:10.1080/07350015.2023.2257270> for further descriptions of FNETS methodology and Owens, Cho and Barigozzi (2024) <arxiv:2301.11675> accompanying the R package.

Authors:Matteo Barigozzi [aut], Haeran Cho [cre, aut], Dom Owens [aut]

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fnets.pdf |fnets.html✨
fnets/json (API)

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

Bug tracker:https://github.com/haeran-cho/fnets/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • data.restricted - Simulated data from the restricted factor-adjusted vector autoregression model
  • data.unrestricted - Simulated data from the unrestricted factor-adjusted vector autoregression model

On CRAN:

Conda-Forge:

factor-modelsforecastinghigh-dimensionalnetwork-estimationtime-seriesvector-autoregressioncpp

5.33 score 7 stars 28 scripts 299 downloads 11 exports 29 dependencies

Last updated 4 months agofrom:89da3c3b59. Checks:1 OK, 10 WARNING. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 18 2025
R-4.5-win-x86_64WARNINGFeb 18 2025
R-4.5-mac-x86_64WARNINGFeb 18 2025
R-4.5-mac-aarch64WARNINGFeb 18 2025
R-4.5-linux-x86_64WARNINGFeb 18 2025
R-4.4-win-x86_64WARNINGFeb 18 2025
R-4.4-mac-x86_64WARNINGFeb 18 2025
R-4.4-mac-aarch64WARNINGFeb 18 2025
R-4.3-win-x86_64WARNINGFeb 18 2025
R-4.3-mac-x86_64WARNINGFeb 18 2025
R-4.3-mac-aarch64WARNINGFeb 18 2025

Exports:cv_truncfactor.numberfnetsfnets.factor.modelfnets.varnetworkpar.lrpcsim.restrictedsim.unrestrictedsim.varthreshold

Dependencies:clicodetoolscpp11doParalleldotCall64fieldsforeachglmnetglueigraphiteratorslatticelifecyclelpSolvemagrittrmapsMASSMatrixpkgconfigplyrRColorBrewerRcppRcppEigenrlangshapespamsurvivalvctrsviridisLite