Other packages > Find by keyword >

HDMFA  

High-Dimensional Matrix Factor Analysis
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("HDMFA", "0.1.1")



Attach the package and use:
library("HDMFA")
Maintained by
Ran Zhao
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-05-30
Latest Update: 2023-05-30
Description:
High-dimensional matrix factor models have drawn much attention in view of the fact that observations are usually well structured to be an array such as in macroeconomics and finance. In addition, data often exhibit heavy-tails and thus it is also important to develop robust procedures. We aim to address this issue by replacing the least square loss with Huber loss function. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's Frobenius norm, which leads to a weighted iterative projection approach to compute and learn the parameters and thereby named as Robust-Matrix-Factor-Analysis (RMFA), see the details in He et al. (2023). The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm (IHR), see the details in He et al. (2023) . In this package, we also provide the algorithm for alpha-PCA by Chen & Fan (2021) , the Projected estimation (PE) method by Yu et al. (2022). In addition, the methods for determining the pair of factor numbers are also given.
How to cite:
Ran Zhao (2023). HDMFA: High-Dimensional Matrix Factor Analysis. R package version 0.1.1, https://cran.r-project.org/web/packages/HDMFA. Accessed 23 Dec. 2024.
Previous versions and publish date:
0.1.0 (2023-05-30 12:50)
Other packages that cited HDMFA R package
View HDMFA citation profile
Other R packages that HDMFA depends, imports, suggests or enhances
Complete documentation for HDMFA
Functions, R codes and Examples using the HDMFA R package
Full HDMFA 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

dMod  
Dynamic Modeling and Parameter Estimation in ODE Models
The framework provides functions to generate ODEs of reaction networks, parameter transformations, ...
Download / Learn more Package Citations See dependency  
gemma2  
GEMMA Multivariate Linear Mixed Model
Fits a multivariate linear mixed effects model that uses a polygenic term, after Zhou & Stephens (20 ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  
compound.Cox  
Univariate Feature Selection and Compound Covariate for Predicting Survival, Including Copula-Based Analyses for Dependent Censoring
Univariate feature selection and compound covariate methods under the Cox model with high-dimensiona ...
Download / Learn more Package Citations See dependency  
GNAR  
Methods for Fitting Network Time Series Models
Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models ...
Download / Learn more Package Citations See dependency  
fasano.franceschini.test  
Fasano-Franceschini Test: A Multivariate Kolmogorov-Smirnov Two-Sample Test
An implementation of the two-sample multivariate Kolmogorov-Smirnov test described by Fasano and Fr ...
Download / Learn more Package Citations See dependency  

23,394

R Packages

201,798

Dependencies

63,416

Author Associations

23,395

Publication Badges

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