Package: hdiVAR 1.0.2

Xiang Lyu

hdiVAR: Statistical Inference for Noisy Vector Autoregression

The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2023). "Statistical inference for high-dimensional vector autoregression with measurement error", Statistica Sinica.

Authors:Xiang Lyu [aut, cre], Jian Kang [aut], Lexin Li [aut]

hdiVAR_1.0.2.tar.gz
hdiVAR_1.0.2.tar.gz(r-4.5-noble)hdiVAR_1.0.2.tar.gz(r-4.4-noble)
hdiVAR_1.0.2.tgz(r-4.4-emscripten)hdiVAR_1.0.2.tgz(r-4.3-emscripten)
hdiVAR.pdf |hdiVAR.html
hdiVAR/json (API)

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

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 3 scripts 171 downloads 7 exports 2 dependencies

Last updated 2 years agofrom:b10b8997ca. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 23 2024
R-4.5-linuxOKDec 23 2024

Exports:CV_VARMLEEstephdVARtestkalmanMstepsEMVARMLE

Dependencies:abindlpSolve

Vignette of R package hdiVAR

Rendered fromhdiVAR.Rmdusingknitr::rmarkdownon Dec 23 2024.

Last update: 2023-05-14
Started: 2020-10-07