Provides tools for drawing Statistical Process Control (SPC) charts. This package supports the NHS 'Making Data Count' programme, and allows users to draw XmR charts, use change points and apply rules with summary indicators for when rules are breached.

Waiting list management using queuing theory to analyse, predict and manage queues, based on the approach described in Fong et al. (2022) <doi:10.1101/2022.08.23.22279117>.  Aimed at UK National Health Service (NHS) applications, waiting list summary statistics, target-value calculations, waiting list simulation, and scheduling functions are included.

Illustrate graphically the most common Null Hypothesis Significance Testing procedures. More specifically, this package provides functions to plot Chi-Squared, F, t (one- and two-tailed) and z (one- and two-tailed) tests, by plotting the probability density under the null hypothesis as a function of the different test statistic values. Although highly flexible (color theme, fonts, etc.), only the minimal number of arguments (observed test statistic, degrees of freedom) are necessary for a clear and useful graph to be plotted, with the observed test statistic and the p value, as well as their corresponding value labels. The axes are automatically scaled to present the relevant part and the overall shape of the probability density function. This package is especially intended for education purposes, as it provides a helpful support to help explain the Null Hypothesis Significance Testing process, its use and/or shortcomings.

The implementation of Markov Model Multiple Imputation
  with the application to COVID-19 scale, NIAID OS.

Framework is devoted to mining numerical association rules through the
  utilization of nature-inspired algorithms for optimization. Drawing inspiration
  from the 'NiaARM' 'Python' and the 'NiaARM' 'Julia' packages, this repository
  introduces the capability to perform numerical association rule mining in
  the R programming language.
  Fister Jr., Iglesias, Galvez, Del Ser, Osaba and Fister (2018) <doi:10.1007/978-3-030-03493-1_9>.