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GCCfactor

The goal of GCCfactor is to implement estimation, model selection, and inference, as developed in "Generalised canonical correlation estimation of the multilevel factor model".

If you encounter a bug, please file an issue on the GitHub Issues page.

Installation

You can install the development version of GCCfactor from GitHub with:

# install.packages("pak")
pak::pak("rl1081/GCCfactor")

or install from CRAN:

install.packages("GCCfactor")

Example

library(GCCfactor)

panel <- UKhouse 
est_multi <- multilevel(panel, ic = "BIC3", standarise = TRUE, r_max = 5,
                        overestimate = TRUE, depvar_header = "dlPrice", 
                        i_header = "Region", j_header = "LPA_Type", t_header = "Date")

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Version

Install

install.packages('GCCfactor')

Monthly Downloads

300

Version

1.2.1

License

GPL (>= 3)

Maintainer

Rui Lin

Last Published

July 28th, 2026

Functions in GCCfactor (1.2.1)

vcov_global_loading

Get the covariance estimates for the global factor loadings
vcov_local_factor

Get the covariance estimates for the local factors
vcov_local_comp

Get the variance estimates of the local component
vcov_global_comp

Get the variance estimates of the global component
multilevel

Full estimation of the multilevel factor model
GCC

Generalised canonical correlation estimation for the global factors
summary.multi_result

Print the relative importance ratios
UKhouse

England and Wales House Price Growth Data Categorised by Regions
vcov_POET

POET estimation of the covariance for the error terms
PC

Principal component (PC) estimation of the approximate factor model
check_data

Check validity of the data and headers
panel2list

data.frame to list of data matrices
infocrit

Selection criteria for the approximate factor model
vcov_local_loading

Get the covariance estimates for the local factor loadings
vcov_global_factor

Get the covariance estimates for the global factors