Description: Provides functions for the joint analysis of K sets
of p-values obtained for a same list of items. This joint analysis is
performed by querying a composed hypothesis, i.e. an arbitrary complex
combination of simple hypotheses, as described in Mary-Huard et al.
(2021) <arXiv:2104.14601>. The null distribution corresponding to the
composed hypothesis of interest is obtained by fitting non-parametric
mixtures models (one for each of the simple hypothesis of the complex
combination). Type I error rate control is achieved through Bayesian
False Discovery Rate control.  The 3 main functions of the package
GetHinfo(), qch.fit() and qch.test() correspond to the 3 steps for
querying a composed hypothesis (composed H0/H1 formulation, inferring
the null distribution and testing the null hypothesis).
Author: Tristan Mary-Huard [aut, cre] (<https://orcid.org/0000-0002-3839-9067>)

Title: Robust Estimation of Gaussian Mixture Models
Description: Robust estimation of Gaussian mixture models fitted by modified EM algorithm, robust clustering and classification.

person("Giuseppe", "Lamberti", email = "giuseppelamb@hotmail.com", role = c("aut", "cre")),
person("Cristina", "Davino", email = "cdavino@unina.it",  role = "ctb"),
person("Pasquale", "Dolce", email = "pasquale.dolce@unina.it", role = "ctb"),
person("Domenico", "Vistocco", email = "domenico.vistocco@unina.it", role ="ctb"))
Description: Implements the Quantile Composite-based Path Modeling approach
Dolce et al., 2021 <doi:10.1007/s11634-021-00469-0>). The method complement the traditional PLS Path Modeling approach, analyzing the entire distribution of outcome variables and, thus, overcoming the classical exploration of only average effects. It exploits quantile regression to investigate changes in the relationships among constructs and and between constructs and observed variables according to the analyzed quantile of interest.
Packaged: 2022-02-23 13:17:49 UTC; giuseppelamberti

Dolce et al., 2021 <doi:10.1007/s11634-021-00469-0>). The method complements the traditional PLS Path Modeling approach, analyzing the entire distribution of outcome variables and, therefore, overcoming the classical exploration of only average effects. It exploits quantile regression to investigate changes in the relationships among constructs and between constructs and observed variables.
Packaged: 2022-04-06 10:43:00 UTC; giuseppelamberti