Helps to prepare a release. Before releasing an R package it is important to update the DESCRIPTION file and the changelog. This package prepares these files and also updates the versions according to the branches. It relies heavily on the 'desc' packages.

Functions to obtain an important number of electoral indicators described in the package, which can be divided into two large sections: The first would be the one containing the indicators of electoral disproportionality, such as, Rae index, Loosemore–Hanby index, etc. The second group is intended to study the dimensions of the party system vote, through the indicators of electoral fragmentation, polarization, volatility, etc. Moreover, multiple seat allocation simulations can also be performed based on different allocation systems, such as the D'Hondt method, Sainte-Laguë, etc. Finally, some of these functions have been built so that, if the user wishes, the data provided by the Spanish Ministry of Home Office for different electoral processes held in Spain can be obtained automatically. All the above will allow the users to carry out deep studies on the results obtained in any type of electoral process. The methods are described in: Oñate, Pablo and Ocaña, Francisco A. (1999, ISBN:9788474762815); Ruiz Rodríguez, Leticia M. and Otero Felipe, Patricia (2011, ISBN:9788474766226).

Calculates relevance and significance values for
  simple models and for many types of regression models.
  These are introduced in
  'Stahel, Werner A.' (2021)
  "Measuring Significance and Relevance instead of p-values."
  <https://stat.ethz.ch/~stahel/relevance/stahel-relevance2103.pdf>.
  These notions are also applied to replication studies,
  as described in the manuscript
  'Stahel, Werner A.' (2022)
  "'Replicability': Terminology, Measuring Success, and Strategy"
  available in the documentation.

Tools to fit and simulate realizations from relational event models.

A method for fitting the entire regularization path of the
    reluctant generalized additive model (RGAM) for linear regression, logistic, 
    Poisson and Cox regression models. See Tay, J. K., and Tibshirani, R., (2019)
    <arXiv:1912.01808> for details.