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.

Version: | 1.1 |

Imports: | coda, HDInterval, lpSolve |

Published: | 2020-04-15 |

Author: | Panagiotis Papastamoulis [aut, cre] |

Maintainer: | Panagiotis Papastamoulis <papapast at yahoo.gr> |

License: | GPL-2 |

NeedsCompilation: | no |

Citation: | factor.switching citation info |

CRAN checks: | factor.switching results |

Reference manual: | factor.switching.pdf |

Package source: | factor.switching_1.1.tar.gz |

Windows binaries: | r-devel: factor.switching_1.1.zip, r-release: factor.switching_1.1.zip, r-oldrel: factor.switching_1.1.zip |

macOS binaries: | r-release (arm64): factor.switching_1.1.tgz, r-release (x86_64): factor.switching_1.1.tgz, r-oldrel: factor.switching_1.1.tgz |

Old sources: | factor.switching archive |

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