Other packages > Find by keyword >

TensorPreAve  

Rank and Factor Loadings Estimation in Time Series Tensor Factor Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("TensorPreAve", "1.1.0")



Attach the package and use:
library("TensorPreAve")
Maintained by
Weilin Chen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-11-08
Latest Update: 2023-04-14
Description:
A set of functions to estimate rank and factor loadings of time series tensor factor models. A tensor is a multidimensional array. To analyze high-dimensional tensor time series, factor model is a major dimension reduction tool. 'TensorPreAve' provides functions to estimate the rank of core tensors and factor loading spaces of tensor time series. More specifically, a pre-averaging method that accumulates information from tensor fibres is used to estimate the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves. A new rank estimation method is also implemented to utilizes correlation information from the projected data. See Chen and Lam (2023) <doi:10.48550/arXiv.2208.04012> for more details.
How to cite:
Weilin Chen (2022). TensorPreAve: Rank and Factor Loadings Estimation in Time Series Tensor Factor Models. R package version 1.1.0, https://cran.r-project.org/web/packages/TensorPreAve. Accessed 05 Jan. 2025.
Previous versions and publish date:
0.1.1 (2022-11-08 15:20)
Other packages that cited TensorPreAve R package
View TensorPreAve citation profile
Other R packages that TensorPreAve depends, imports, suggests or enhances
Complete documentation for TensorPreAve
Functions, R codes and Examples using the TensorPreAve R package
Some associated functions: bs_cor_rank . equal_weight_tensor . iter_proj . pre_est . rank_factors_est . tensor_data_gen . value_weight_tensor . 
Some associated R codes: bs_cor_rank.R . data.R . iter_proj.R . pre_est.R . rank_factors_est.R . tensor_data_gen.R .  Full TensorPreAve package functions and examples
Downloads during the last 30 days
Get rewarded with contribution points by helping add
Reviews / comments / questions /suggestions ↴↴↴

Today's Hot Picks in Authors and Packages

ENMeval  
Automated Tuning and Evaluations of Ecological Niche Models
Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs ...
Download / Learn more Package Citations See dependency  
interplot  
Plot the Effects of Variables in Interaction Terms
Plots the conditional coefficients ("marginal effects") of variables included in multiplicative int ...
Download / Learn more Package Citations See dependency  
Maintainer: Yue Hu (view profile)
bama  
High Dimensional Bayesian Mediation Analysis
Perform mediation analysis in the presence of high-dimensional mediators based on the potential out ...
Download / Learn more Package Citations See dependency  
uclust  
Clustering and Classification Inference with U-Statistics
Clustering and classification inference for high dimension low sample size (HDLSS) data with U-stati ...
Download / Learn more Package Citations See dependency  
GFisher  
Generalized Fisher's Combination Tests Under Dependence
Accurate and computationally efficient p-value calculation methods for a general family of Fisher ty ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  

23,440

R Packages

202,297

Dependencies

63,567

Author Associations

23,434

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA