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

Provides dictionary-based tools for recoding free-text gender
    responses into consistent categories while preserving gender diversity where
    possible. The package standardises spelling, capitalization, whitespace, and
    common variants through curated named character-vector dictionaries, supports
    either detailed or collapsed output categories, and can retain original
    unmatched responses for manual review. It also includes helpers for creating
    custom dictionaries from approximate string matches and a local interactive
    application for recoding uploaded data files.

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/>.