Package: factor.switching 1.4

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.

Authors:Panagiotis Papastamoulis [aut, cre]

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factor.switching.pdf |factor.switching.html
factor.switching/json (API)

# Install 'factor.switching' in R:
install.packages('factor.switching', repos = c('https://mqbssppe.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.48 score 1 stars 1 packages 3 scripts 300 downloads 9 exports 12 dependencies

Last updated 9 months agofrom:aba479e72b. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 17 2024
R-4.5-winOKOct 17 2024
R-4.5-linuxOKOct 17 2024
R-4.4-winOKOct 17 2024
R-4.4-macOKOct 17 2024
R-4.3-winOKOct 17 2024
R-4.3-macOKOct 17 2024

Exports:compareMultipleChainscredible.regionplot.rspprocrustes_switchingrsp_exactrsp_full_sarsp_partial_saswitch_and_permuteweighted_procrustes_switching

Dependencies:codaHDIntervallatticelpSolveMASSMatrixMatrixModelsmcmcMCMCpackquantregSparseMsurvival