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.

An open, multi-algorithmic pipeline for easy, fast and efficient
  analysis of cellular sub-populations and the molecular signatures that
  characterize them. The pipeline consists of four successive steps: data
  pre-processing, cellular clustering with pseudo-temporal ordering, defining
  differential expressed genes and biomarker identification. More details on
  Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements
  extensions of the work published by Ghannoum et. al. (2019)
  <doi:10.1101/700989>.