{ "_id": "6a2525434b233be19839274a", "Package": "BTtest", "Title": "Estimate the Number of Factors in Large Nonstationary Datasets", "Version": "0.10.3", "Authors@R": "person(\"Paul\", \"Haimerl\", , \"paul.haimerl@econ.au.dk\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-3198-8317\"))", "Maintainer": "Paul Haimerl ", "Description": "Large panel data sets are often subject to common trends.\nHowever, it can be difficult to determine the exact number of\nthese common factors and analyse their properties. The package\nimplements the Barigozzi and Trapani (2022)\n test, which not only\nprovides an efficient way of estimating the number of common\nfactors in large nonstationary panel data sets, but also gives\nfurther insights on factor classes. The routine identifies the\nexistence of (i) a factor subject to a linear trend, (ii) the\nnumber of zero-mean I(1) and (iii) zero-mean I(0) factors.\nFurthermore, the package includes the Integrated Panel Criteria\nby Bai (2004) that provide\na complementary measure for the number of factors.", "License": "GPL (>= 3)", "Encoding": "UTF-8", "BugReports": "https://github.com/Paul-Haimerl/BTtest/issues", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.1", "URL": "https://github.com/Paul-Haimerl/BTtest", "Config/testthat/edition": "3", "Repository": "https://paul-haimerl.r-universe.dev", "Date/Publication": "2025-03-03 21:21:40 UTC", "RemoteUrl": "https://github.com/paul-haimerl/bttest", "RemoteRef": "HEAD", "RemoteSha": "efab0bc37d870d0e212a0df827aeb1dc1c2d6b78", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-07 07:53:47 UTC", "User": "root" }, "Author": "Paul Haimerl [aut, cre] (ORCID:\n)", "MD5sum": "de991d7e03c850c5f89397f58d88c77e", "_user": "paul-haimerl", 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