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.

Design and Analysis for Factorial Experiments
Naoki Egami, Brandon de la Cuesta, Kosuke Imai
Provides design-based and model-based estimators for the population average marginal component effects in general factorial experiments, including conjoint analysis. The package also implements a series of recommendations offered in de la Cuesta, Egami, and Imai (2019+), and Egami and Imai (2019) <doi:10.1080/01621459.2018.1476246>.
prodlim, sandwich, igraph, pbmcapply, pbapply, mvtnorm,
stringr, doParallel, foreach, estimatr

Hierarchical Algorithm for Post-Hoc Testing
c(
person("Agnieszka", "Sitko", email = "ag.agnieszka.sitko@gmail.com", role = c("aut", "cre")),
person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut","ths"))
)
A set of tools to support results of post-hoc testing and enable to extract hierarchical structure of factors. Work on this package was financially supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.
ggplot2, dplyr, reshape2, colorRamps, proxy, MASS, survminer,
gridExtra, scales, mvtnorm, knitr, magrittr, survival,
agricolae, forcats
Agnieszka Sitko [aut, cre],
Przemyslaw Biecek [aut, ths]

ggplot2, dplyr, reshape2, colorRamps, proxy, MASS, ggpubr,
scales, mvtnorm, knitr, magrittr, survival, agricolae,
grDevices

c(
person("Agnieszka", "Sitko", email = "ag.agnieszka.sitko@gmail.com", role = c("aut", "cre")),
person("Aleksandra", "Grudziąż", email = "oll.dabrowska@gmail.com", role = c("aut")),
person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut","ths"))
)
The Merging Path Plot is a methodology for adaptive fusing of k-groups
with likelihood-based model selection. This package contains tools for
exploration and visualization of k-group dissimilarities.
Comparison of k-groups is one of the most important issues
in exploratory analyses and it has zillions of applications.
The traditional approach is to use pairwise post hoc tests
in order to verify which groups differ significantly. However,
this approach fails with a large number of groups in both interpretation
and visualization layer.
The Merging Path Plot solves this problem by using an easy-to-understand
description of dissimilarity among groups based on Likelihood Ratio Test (LRT) statistic.
Work on this package was financially
supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.
ggplot2, dplyr, reshape2, colorRamps, proxy, MASS, ggpubr,
scales, mvtnorm, knitr, magrittr, survival, agricolae, forcats,
formula.tools
Agnieszka Sitko [aut, cre],
Aleksandra Grudziąż [aut],
Przemyslaw Biecek [aut, ths]