Title: Tools for Working with XML Files as R Dataframes
Authors@R: person("Joachim", "Zuckarelli", email = "joachim@zuckarelli.de",
Description: On import, the XML information is converted to a dataframe that reflects the hierarchical XML structure. Intuitive functions allow to navigate within this transparent XML data structure (without any knowledge of 'XPath'). 'flatXML' also provides tools to extract data from the XML into a flat dataframe that can be used to perform statistical operations.

Description: On import, the XML information is converted to a dataframe that reflects the hierarchical XML structure. Intuitive functions allow to navigate within this transparent XML data structure (without any knowledge of 'XPath'). 'flatXML' also provides tools to extract data from the XML into a flat dataframe that can be used to perform statistical operations. It also supports converting dataframes to XML.

Title: Smoother Change Tracking and Versioning for R Packages
Description: Streamlines the process of updating changelogs (NEWS.md)
and versioning R packages developed in git repositories.
rematch2, rlang (>= 0.4.12), tibble, usethis (>= 1.5.0),
Suggests: mockery, covr, fansi, fs, knitr, parsedate, rmarkdown,
rstudioapi, testthat (>= 3.0.0), brio, callr, pkgload
Author: Kirill Müller [aut, cre, cph] (<https://orcid.org/0000-0002-1416-3412>),

Author: Incarbone G., Mazza A., Punzo A., Ingrassia S.
Description: Flexible Cluster Weighted Models.

Author: Mazza A., Punzo A., Ingrassia S.

Description: This package implements a flexible version of the Cluster Weighted Model (CWM), which is an eminent member of the class of mixtures of regression models with random covariates.

Description: Allows for maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates.

Description: Allows maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates.
Methods are described in Angelo Mazza, Antonio Punzo, Salvatore Ingrassia (2018) <doi:10.18637/jss.v086.i02>.