factor.switching
Post-Processing MCMC Outputs of Bayesian Factor Analytic Models
A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. The package applies a series of rotation, sign and permutation transformations (Papastamoulis and Ntzoufras (2022) <DOI:10.1007/s11222-022-10084-4>) into raw MCMC samples of factor loadings, which are provided by the user. The post-processed output is identifiable and can be used for MCMC inference on any parametric function of factor loadings. Comparison of multiple MCMC chains is also possible.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.4 |
rolling linux/jammy R-4.5 | factor.switching_1.4.tar.gz |
96.3 KiB |
1.4 |
rolling linux/noble R-4.5 | factor.switching_1.4.tar.gz |
96.3 KiB |
1.4 |
rolling source/ R- | factor.switching_1.4.tar.gz |
22.2 KiB |
1.4 |
latest linux/jammy R-4.5 | factor.switching_1.4.tar.gz |
96.3 KiB |
1.4 |
latest linux/noble R-4.5 | factor.switching_1.4.tar.gz |
96.3 KiB |
1.4 |
latest source/ R- | factor.switching_1.4.tar.gz |
22.2 KiB |
1.4 |
2026-04-26 source/ R- | factor.switching_1.4.tar.gz |
22.2 KiB |
1.4 |
2026-04-23 source/ R- | factor.switching_1.4.tar.gz |
22.2 KiB |
1.4 |
2026-04-09 windows/windows R-4.5 | factor.switching_1.4.zip |
99.9 KiB |
1.4 |
2025-04-20 source/ R- | factor.switching_1.4.tar.gz |
22.2 KiB |