abind, cluster, dendextend, FactoMineR, ggpubr(>= 0.1.5),
grid, stats, reshape2, ggrepel, tidyr
'cluster_utilities.R' 'decathlon2.R' 'print.factoextra.R'
'get_hmfa.R' 'fviz_hmfa.R' 'get_mfa.R' 'fviz_mfa.R'
'deprecated.R' 'fviz_add.R' 'eigenvalue.R' 'utilities.R'
'dist.R' 'fviz_dend.R' 'hcut.R' 'get_pca.R' 'fviz_cluster.R'
'eclust.R' 'facto_summarize.R' 'fviz.R' 'fviz_ca.R'
'fviz_contrib.R' 'fviz_cos2.R' 'fviz_ellipses.R' 'fviz_famd.R'
'get_mca.R' 'fviz_mca.R' 'fviz_mclust.R' 'fviz_nbclust.R'
'fviz_pca.R' 'fviz_silhouette.R' 'get_ca.R'
'get_clust_tendency.R' 'get_famd.R' 'hkmeans.R' 'housetasks.R'
'multishapes.R' 'poison.R' 'zzz.R'

Factorial Optimal Designs for Two-Colour cDNA Microarray
Experiments
Dibaba Bayisa Gemechu, Legesse Kassa Debusho, and Linda Haines
Computes factorial A-, D- and E-optimal designs for two-colour cDNA microarray experiments.

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 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.

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.