Provides a function that generates a customized correlation matrix based on limit values and proportions for intervals composed by its limits. It can also generate random matrices with low, medium, and high correlations, in which low, medium, and high thresholds are user-defined.

Allows users to generate a gendered language score according to the gendered language dictionary in Roberts and Utych (2019) <doi:10.1177/1065912919874883>.

Set of functions to create datasets using a correlation matrix.

Infers state-recorded gender categories from first names and dates of birth using historical
    datasets. By using these datasets instead of lists of male and female names,
    this package is able to more accurately infer the gender of a name, and it
    is able to report the probability that a name was male or female. GUIDELINES:
    This method must be used cautiously and responsibly. Please be sure to see the
    guidelines and warnings about usage in the 'README' or the package documentation.
    See Blevins and Mullen (2015) <http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html>.

Provides an interface to the 'GenderAPI.io' web service (<https://www.genderapi.io>) for determining gender from personal names, email addresses, or social media usernames. Functions are available to submit single or batch queries and retrieve additional information such as accuracy scores and country-specific gender predictions. This package simplifies integration of 'GenderAPI.io' into R workflows for data cleaning, user profiling, and analytics tasks.

A method to predict and report gender from Brazilian first names
    using the Brazilian Institute of Geography and Statistics' Census data.

Implementation of functions, which combines binomial calculation 
    and data visualisation, to analyse the differences in publishing authorship
    by gender described in Day et al. (2020) <doi:10.1039/C9SC04090K>.
    It should only be used when self-reported gender is unavailable.

Provides tools for quantitative analysis in gender studies, including functions to calculate various gender inequality metrics such as the Gender Pay Gap, Gender Inequality Index (GII), Gender Development Index (GDI), and Gender Empowerment Measure (GEM). Also includes extracted secondary example datasets for practice and learning purposes, which were obtained from the UNDP Human Development Reports Data Center and the World Bank Gender Data Portal by the author the dataset is available on <doi:10.34740/kaggle/dsv/6359326>. References: Miller, Kevin; Vagins, Deborah J. (2021) <https://eric.ed.gov/?id=ED596219>. Jacques Charmes & Saskia Wieringa (2003) <doi:10.1080/1464988032000125773>. Gaëlle Ferrant (2010) <https://shs.hal.science/halshs-00462463/>.