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>.

Provides tools for differentially private principal component
    analysis (PCA) visualization. It includes functions for estimating private
    principal component directions, constructing private scree and proportion
    of variance explained summaries, and visualizing two-dimensional PCA score
    summaries using additive and sparse histogram mechanisms. Group-wise score
    visualizations and an interactive 'shiny' app are also provided. Private
    principal component directions are based on Kim and Jung (2025)
    <doi:10.1002/sam.70053>. Private scree summaries use mechanisms motivated
    by Dwork and Roth (2014) <doi:10.1561/0400000042>, Ramsay and Spicker
    (2025) <doi:10.48550/arXiv.2501.14095>, and Yu, Ren and Zhou (2024)
    <doi:10.3150/23-BEJ1706>. Private score plot frames use smooth sensitivity
    quantiles from Nissim, Raskhodnikova and Smith (2007)
    <doi:10.1145/1250790.1250803>. Private score histograms use additive and
    sparse histogram ideas from Wasserman and Zhou (2010)
    <doi:10.1198/jasa.2009.tm08651> and Karwa and Vadhan (2018)
    <doi:10.4230/LIPIcs.ITCS.2018.44>.