Title: Design and Analysis for Factorial Experiments
Author: Naoki Egami, Brandon de la Cuesta, Kosuke Imai
Description: 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>.
Imports: prodlim, sandwich, igraph, pbmcapply, pbapply, mvtnorm,

Title: Hierarchical Algorithm for Post-Hoc Testing
person("Agnieszka", "Sitko", email = "ag.agnieszka.sitko@gmail.com", role = c("aut", "cre")),
Description: 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'.
Imports: ggplot2, dplyr, reshape2, colorRamps, proxy, MASS, survminer,
gridExtra, scales, mvtnorm, knitr, magrittr, survival,
Packaged: 2017-06-30 10:08:38 UTC; agnieszka

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

person("Aleksandra", "Grudziąż", email = "oll.dabrowska@gmail.com", role = c("aut")),
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
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.
supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.
scales, mvtnorm, knitr, magrittr, survival, agricolae, forcats,