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 (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 ORCID iD [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: factor.switching_1.1.tgz, r-oldrel: factor.switching_1.1.tgz
Old sources: factor.switching archive


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