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.

Provides functions to access data from public RESTful APIs including 
    'Nager.Date', 'World Bank API', and 'REST Countries API', retrieving real-time or historical 
    data related to Indonesia, such as holidays, economic indicators, and international 
    demographic and geopolitical indicators. The package also includes a curated collection of 
    open datasets focused on Indonesia, covering topics such as consumer prices, poverty 
    probability, food prices by region, tourism destinations, and minimum wage statistics. 
    The package supports reproducible research and teaching by integrating reliable 
    international APIs and structured datasets from public, academic, and government sources. 
    For more information on the APIs, see: 
    'Nager.Date' <https://date.nager.at/Api>, 
    'World Bank API' <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, 
    and 'REST Countries API' <https://restcountries.com/>.

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.