LSTMfactors
Determining the Number of Factors in Exploratory Factor Analysis by LSTM
Description
A method for factor retention using a pre-trained Long Short Term Memory (LSTM) Network, which is originally developed by Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>, is provided. The sample size of the dataset used to train the LSTM model is 1,000,000. Each sample is a batch of simulated response data with a specific latent factor structure. The eigenvalues of these response data will be used as sequential data to train the LSTM. The pre-trained LSTM is capable of factor retention for real response data with a true latent factor number ranging from 1 to 10, that is, determining the number of factors.
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| Flavor | Status |
|---|---|
| r-devel-linux-x86_64-debian-clang | OK |
| r-devel-linux-x86_64-debian-gcc | OK |
| r-devel-linux-x86_64-fedora-clang | OK |
| r-devel-linux-x86_64-fedora-gcc | OK |
| r-devel-windows-x86_64 | OK |
| r-oldrel-macos-arm64 | OK |
| r-oldrel-macos-x86_64 | OK |
| r-oldrel-windows-x86_64 | OK |
| r-patched-linux-x86_64 | OK |
| r-release-linux-x86_64 | OK |
| r-release-macos-arm64 | OK |
| r-release-macos-x86_64 | OK |
| r-release-windows-x86_64 | OK |
Check History
OK 13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Jun 9, 2026
ERROR 12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Jun 8, 2026
package dependencies
Package required but not available: ‘EFAfactors’ See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’ manual.
OK 13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE May 10, 2026
WARNING 12 OK · 0 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE May 9, 2026
PDF version of manual
LaTeX errors when creating PDF version. This typically indicates Rd problems. LaTeX errors found:
OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
Dependency Network
Version History
1 trackedR Observatory began tracking this package on Mar 10, 2026; it first appeared on CRAN Jul 7, 2025. Releases before tracking aren’t shown.