Estimate the Šesták–Berggren kinetic model (degradation model) from experimental data.
  A closed-form (analytic) solution to the degradation model is implemented as a non-linear fit,
  allowing for the extrapolation of the degradation of a drug product - both in time and 
  temperature. Parametric bootstrap, with kinetic parameters drawn from the multivariate
  t-distribution, and analytical formulae (the delta method) are available options to calculate
  the confidence and prediction intervals.
  The results (modelling, extrapolations and statistical intervals) can be visualised
  with multiple plots. The examples illustrate the accelerated stability modelling in drugs and
  vaccines development.

Allows clinicians to predict the rate and severity of future acute exacerbation in Chronic Obstructive Pulmonary Disease (COPD) patients, based on the clinical prediction models published in Adibi et al. (2020) <doi:10.1016/S2213-2600(19)30397-2> and Safari et al. (2022) <doi:10.1016/j.eclinm.2022.101574>.

Provides functionality for creating and
	evaluating acceptance sampling plans. Sampling plans can be single,
	double or multiple.

Provides a function that implements the acceptance-rejection method in an optimized manner to generate pseudo-random observations for discrete or continuous random variables. Proposed by von Neumann J. (1951), <https://mcnp.lanl.gov/pdf_files/>, the function is optimized to work in parallel on Unix-based operating systems and performs well on Windows systems. The acceptance-rejection method implemented optimizes the probability of generating observations from the desired random variable, by simply providing the probability function or probability density function, in the discrete and continuous cases, respectively. Implementation is based on references CASELLA, George at al. (2004) <https://www.jstor.org/stable/4356322>,  NEAL, Radford M. (2003) <https://www.jstor.org/stable/3448413> and Bishop, Christopher M. (2006, ISBN: 978-0387310732).

A set of fast and convenient functions to help conducting
    accessibility analyses. Given a pre-computed travel cost matrix and a
    land use dataset (containing the location of jobs, healthcare and
    population, for example), the package allows one to calculate
    accessibility levels and accessibility poverty and inequality. The
    package covers the majority of the most commonly used accessibility
    measures (such as cumulative opportunities, gravity-based and floating
    catchment areas methods), as well as the most frequently used
    inequality and poverty metrics (such as the Palma ratio, the
    concentration and Theil indices and the FGT family of measures).