By systematically aggregating and processing textual reports from earthquakes, floods, storms,
  wildfires, and other natural disasters, the framework enables a holistic assessment of crisis narratives.  
  Intelligent cleaning and normalization techniques transform raw commentary into structured data, ensuring
  precise extraction of disaster-specific insights. Collective sentiments of affected communities are  
  quantitatively scored and qualitatively categorized, providing a multifaceted view of societal responses  
  under duress. Interactive geographic maps and temporal charts illustrate the evolution and spatial dispersion
  of emotional reactions and impact indicators.

Access and manage the application programming interface (API) of the United Nations Office for the Coordination of Humanitarian Affairs' (OCHA) ReliefWeb disaster events at <https://reliefweb.int/disasters>. The package requires a minimal number of dependencies. It offers functionality to retrieve a user-defined sample of disaster events from ReliefWeb, providing an easy alternative to scraping the ReliefWeb website. It enables a seamless integration of regular data updates into the research work flow.

Estimation of incidence and case fatality for a chronic disease, given partial information, using a multi-state model. Given data on age-specific mortality and either incidence or prevalence, Bayesian inference is used to estimate the posterior distributions of incidence, case fatality, and functions of these such as prevalence.  The methods are described in Jackson et al. (2023) <doi:10.1093/jrsssa/qnac015>.

Area under the curve (AUC; Myerson et al., 2001) <doi:10.1901/jeab.2001.76-235> 
  is a popular measure used in discounting research. Although the calculation of
  AUC is standardized, there are differences in AUC based on some assumptions. 
  For example, Myerson et al. (2001) <doi:10.1901/jeab.2001.76-235> 
  assumed that (with delay discounting data) a researcher would impute an
  indifference point at zero delay equal to the value of the larger, later outcome.
  However, this practice is not clearly followed. This imputed zero-delay indifference
  point plays an important role in log and ordinal versions of AUC.
  Ordinal and log versions of AUC are described by Borges et al. (2016)<doi:10.1002/jeab.219>. 
  The package can calculate all three versions of AUC [and includes a new version: IHS(AUC)],
  impute indifference points when x = 0, calculate ordinal AUC in the case of Halton
  sampling of x-values, and account for probability discounting AUC.