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hdiVAR  

Statistical Inference for Noisy Vector Autoregression
View on CRAN: Click here


Download and install hdiVAR package within the R console
Install from CRAN:
install.packages("hdiVAR")

Install from Github:
library("remotes")
install_github("cran/hdiVAR")

Install by package version:
library("remotes")
install_version("hdiVAR", "1.0.2")



Attach the package and use:
library("hdiVAR")
Maintained by
Xiang Lyu
[Scholar Profile | Author Map]
First Published: 2020-10-07
Latest Update: 2023-05-14
Description:
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.
How to cite:
Xiang Lyu (2020). hdiVAR: Statistical Inference for Noisy Vector Autoregression. R package version 1.0.2, https://cran.r-project.org/web/packages/hdiVAR. Accessed 02 Apr. 2025.
Previous versions and publish date:
1.0.1 (2020-10-07 14:20)
Other packages that cited hdiVAR R package
View hdiVAR citation profile
Other R packages that hdiVAR depends, imports, suggests or enhances
Complete documentation for hdiVAR
Functions, R codes and Examples using the hdiVAR R package
Some associated functions: CV_VARMLE . Estep . Mstep . VARMLE . hdVARtest . kalman . sEM . 
Some associated R codes: CV_VARMLE.R . Estep.R . Mstep.R . VARMLE.R . entry_var.R . global_pval.R . hdVARtest.R . kalman.R . sEM.R . simul_FDR.R .  Full hdiVAR package functions and examples
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