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 (2020) <arXiv:2004.05105>) 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.
|Author||Panagiotis Papastamoulis [aut, cre] (<https://orcid.org/0000-0001-9468-7613>)|
|Maintainer||Panagiotis Papastamoulis <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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