This MCMC method takes a data numeric vector (Y) and assigns the elements of Y
  to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred.
  Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.

An implementation of common statistical analysis and models with
    differential privacy (Dwork et al., 2006a) <doi:10.1007/11681878_14>
    guarantees. The package contains, for example, functions providing
    differentially private computations of mean, variance, median, histograms,
    and contingency tables. It also implements some statistical models and
    machine learning algorithms such as linear regression (Kifer et al., 2012)
    <https://proceedings.mlr.press/v23/kifer12.html>
    and SVM (Chaudhuri et al., 2011)
    <https://jmlr.org/papers/v12/chaudhuri11a.html>. In addition, it implements
    some popular randomization mechanisms, including
    the Laplace mechanism (Dwork et al., 2006a)
    <doi:10.1007/11681878_14>, Gaussian mechanism (Dwork et al., 2006b)
    <doi:10.1007/11761679_29>, analytic Gaussian mechanism (Balle & Wang, 2018)
    <https://proceedings.mlr.press/v80/balle18a.html>, and exponential mechanism
    (McSherry & Talwar, 2007) <doi:10.1109/FOCS.2007.66>.

Multivariate Gaussian mixture model with a determinant point 
  process prior to promote the discovery of parsimonious components from 
  observed data. See Xu, Mueller, Telesca (2016) <doi:10.1111/biom.12482>.

Computations for approximations and alternatives for the 'DPQ'
  (Density (pdf), Probability (cdf) and Quantile) functions for probability
  distributions in R.
  Primary focus is on (central and non-central) beta, gamma and related
  distributions such as the chi-squared, F, and t.
  --
  For several distribution functions, provide functions implementing formulas from
  Johnson, Kotz, and Kemp (1992) <doi:10.1002/bimj.4710360207>  and
  Johnson, Kotz, and Balakrishnan (1995) for discrete or continuous
  distributions respectively.
  This is for the use of researchers in these numerical approximation
  implementations, notably for my own use in order to improve standard
  R pbeta(), qgamma(), ..., etc: {'"dpq"'-functions}.