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.

Version: 1.4
Imports: coda, HDInterval, lpSolve , MCMCpack
Published: 2024-02-12
DOI: 10.32614/CRAN.package.factor.switching
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_1.4.tar.gz
Windows binaries: r-devel: factor.switching_1.4.zip, r-release: factor.switching_1.4.zip, r-oldrel: factor.switching_1.4.zip
macOS binaries: r-release (arm64): factor.switching_1.4.tgz, r-oldrel (arm64): factor.switching_1.4.tgz, r-release (x86_64): factor.switching_1.4.tgz, r-oldrel (x86_64): factor.switching_1.4.tgz
Old sources: factor.switching archive

Reverse dependencies:

Reverse imports: DGP4LCF


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