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]

fnets_0.2.0.tar.gz
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fnets_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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:

factor-models forecasting high-dimensional network-estimation time-series vector-autoregression cpp

4.59 score 10 stars 39 scripts 241 downloads 11 exports 29 dependencies

Last updated from:89da3c3b59. Checks:11 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	WARNING	181
linux-devel-x86_64	WARNING	202
source / vignettes	OK	178
linux-release-arm64	WARNING	175
linux-release-x86_64	WARNING	174
macos-release-arm64	WARNING	154
macos-release-x86_64	WARNING	300
macos-oldrel-arm64	WARNING	133
macos-oldrel-x86_64	WARNING	227
windows-devel	WARNING	162
windows-release	WARNING	200
windows-oldrel	WARNING	158
wasm-release	OK	116

Exports:cv_trunc factor.number fnets fnets.factor.model fnets.var network par.lrpc sim.restricted sim.unrestricted sim.var threshold

Dependencies:cli codetools cpp11 doParallel dotCall64 fields foreach glmnet glue igraph iterators lattice lifecycle lpSolve magrittr maps MASS Matrix pkgconfig plyr RColorBrewer Rcpp RcppEigen rlang shape spam survival vctrs viridisLite

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Sample Covariance Function Without Centering	acf_no_center
Truncate data, with truncation parameter chosen by cross-validation.	cv_trunc
Simulated data from the restricted factor-adjusted vector autoregression model	data.restricted
Simulated data from the unrestricted factor-adjusted vector autoregression model	data.unrestricted
Factor number selection methods	factor.number
Factor-adjusted network estimation	fnets
Factor model estimation	fnets.factor.model
'l1'-regularised Yule-Walker estimation for VAR processes	fnets.var
Convert networks into igraph objects	network
Convert networks estimated by fnets into igraph objects	network.fnets
Parametric estimation of long-run partial correlations of factor-adjusted VAR processes	par.lrpc
Plot factor number	plot.factor.number
Plotting the networks estimated by fnets	plot.fnets
Plotting the thresholding procedure	plot.threshold
Forecasting for factor models	predict.fm
Forecasting by fnets	predict.fnets
Print factor number	print.factor.number
Print factor model	print.fm
Print fnets	print.fnets
Print threshold	print.threshold
Simulate data from a restricted factor model	sim.restricted
Simulate data from an unrestricted factor model	sim.unrestricted
Simulate a VAR(1) process	sim.var
Threshold the entries of the input matrix at a data-driven level	threshold
Sample Covariance Function Without Centering, after truncating data.	truncateAndComputeCovariance_lag