Imputation for the missing count values in accelerometer data. The methodology includes both parametric and semi-parametric multiple imputations under the zero-inflated Poisson lognormal model. This package also provides multiple functions to pre-process the accelerometer data previous to the missing data imputation. These includes detecting wearing and non-wearing time, selecting valid days and subjects, and creating plots.

Estimate the Šesták–Berggren kinetic model (degradation model) from experimental data. A
  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).