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 for example the location of jobs, healthcare and 
    population), 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), some cutting edge 
    measures proposed in the literature (e.g. balancing cost and constrained 
    accessibility) 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).