Functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.

Performs a unified approach for obtaining and estimating
    unidimensional  and multidimensional Item Response Theory (IRT) 
    Dual Models (DMs), proposed by Ferrando (2019 <doi:10.1177/0146621618817779>).

It provides a general framework to analyse dependence between point processes in time. It includes parametric and non-parametric tests to study independence,  and functions for generating and analysing  different types of dependence.

Companion package to the book "industRial data science", 
    J.Ramalho (2021) <https://j-ramalho.github.io/industRial/>. 
    Provides data sets and functions to complete the case studies and contains 
    the book original Rmd files and tutorials.

Provides functions to download and parse information from INEGI
    (Official Mexican statistics agency). To learn more about the API, see <https://www.inegi.org.mx/servicios/api_indicadores.html>.

Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).

Decomposition of income inequality by groups formed of individuals 
    possessing similar characteristics (e.g., sex, education, age) and their 
    income sources at the same time.
    Decomposition of the Theil index is based on Giammatteo, M. (2007) 
    <https://www.lisdatacenter.org/wps/liswps/466.pdf>.
    Decomposition of the squared coefficient of variation is based on
    Garcia-Penalosa, C., & Orgiazzi, E. (2013) <doi:10.1111/roiw.12054>.