paul-haimerl / bttest Goto Github PK
View Code? Open in Web Editor NEWEstimate the number of factors in large nonstationary datasets
Home Page: https://doi.org/10.1080/07350015.2021.1901719
License: GNU General Public License v3.0
Estimate the number of factors in large nonstationary datasets
Home Page: https://doi.org/10.1080/07350015.2021.1901719
License: GNU General Public License v3.0
First release:
usethis::use_news_md()
usethis::use_cran_comments()
Title:
and Description:
@return
and @examples
Authors@R:
includes a copyright holder (role 'cph')Prepare for release:
git pull
urlchecker::url_check()
devtools::build_readme()
devtools::check(remote = TRUE, manual = TRUE)
devtools::check_win_devel()
git push
Submit to CRAN:
usethis::use_version('minor')
devtools::submit_cran()
Wait for CRAN...
usethis::use_github_release()
usethis::use_dev_version(push = TRUE)
usethis::use_news_md()
Sorry -- just accidentally came across your package because it was recently updated on CRAN, and wanted to check whether it might help with something I was working on. So this question is probably not so much an issue per se as just me not being familiar with the theory involved.
But, is it expected that the results are not deterministic? If it is, it might be worth mentioning in the package documentation (or if there is some parameter like the number of simulations that can help, perhaps it could be exposed in the API?)
X <- sim_DGP(N = 100, n_Periods = 200)
bb <- t(sapply(1:100, function(x) BTtest(X = X, r_max = 10, alpha = 0.05, BT1 = TRUE)))
nrow(unique(bb)) # 4
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