A tool to sample data with the desired properties.Samples can be 
    drawn by purposive sampling with determining distributional 
    conditions, such as deviation from normality (skewness and kurtosis), 
    and sample size in quantitative research studies.
    For purposive sampling, a researcher has something in mind and participants that 
    fit the purpose of the study are included (Etikan,Musa, & Alkassim, 2015) 
    <doi:10.11648/j.ajtas.20160501.11>.Purposive sampling can be useful for answering
    many research questions (Klar & Leeper, 2019) 
    <doi:10.1002/9781119083771.ch21>.

We offer an implementation of the series representation put 
  forth in "A series representation for multidimensional 
  Rayleigh distributions" by Wiegand and Nadarajah <DOI: 
  10.1002/dac.3510>. Furthermore we have implemented an 
  integration approach proposed by Beaulieu et al. for 3 and 
  4-dimensional Rayleigh densities (Beaulieu, Zhang,  "New 
  simplest exact forms for the 3D and 4D multivariate Rayleigh 
  PDFs with applications to antenna array geometrics", <DOI: 
  10.1109/TCOMM.2017.2709307>).

Feed longitudinal data into a Bayesian Latent Factor Model to obtain 
  a low-rank representation. Parameters are estimated using a Hamiltonian 
  Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and 
  S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", 
  Actes des JdS 2016.

Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.

We provide a list of functions for replicating the results of the Monte Carlo simulations and empirical application of Jiang et al. (2022). 
    In particular, we provide corresponding functions for generating the three types of random data described in this paper, as well as all the estimation strategies. 
    Detailed information about the data generation process and estimation strategy can be found in Jiang et al. (2022)  <doi:10.48550/arXiv.2201.13004>.