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.
Package details |
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Author | Panagiotis Papastamoulis [aut, cre] (<https://orcid.org/0000-0001-9468-7613>) |
Maintainer | Panagiotis Papastamoulis <papapast@yahoo.gr> |
License | GPL-2 |
Version | 1.4 |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
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