Bayesian inference using the no-U-turn (NUTS) algorithm by 
 Hoffman and Gelman (2014) <https://www.jmlr.org/papers/v15/hoffman14a.html>. 
 Designed for 'AD Model Builder' ('ADMB') models,
 or when R functions for log-density and log-density gradient
 are available, such as 'Template Model Builder'
 models and other special cases. Functionality is similar to 'Stan', 
 and the 'rstan' and 'shinystan' packages are used for diagnostics and 
 inference.

Connect to the 'Adobe Analytics' API v2.0 <https://github.com/AdobeDocs/analytics-2.0-apis>
             which powers 'Analysis Workspace'. The package was developed
             with the analyst in mind, and it will continue to be
             developed with the guiding principles of iterative,
             repeatable, timely analysis.

Calculate users prevalence of a product based on the prevalence of triers in the population. The measurement of triers is relatively easy. It is just a question of whether a person tried a product even once in his life or not. On the other hand, The measurement of people who also adopt it as part of their life is more complicated since adopting an innovative product is a subjective view of the individual. Mickey Kislev and Shira Kislev developed a formula to calculate the prevalence of a product's users to overcome this difficulty. The current package assists in calculating the users prevalence of a product based on the prevalence of triers in the population. See for: Kislev, M. M., and S. Kislev (2020) <doi:10.5539/ijms.v12n4p63>.

An implementation of ADPclust clustering procedures (Fast
    Clustering Using Adaptive Density Peak Detection). The work is built and
    improved upon the idea of Rodriguez and Laio (2014)<DOI:10.1126/science.1242072>. 
    ADPclust clusters data by finding density peaks in a density-distance plot 
    generated from local multivariate Gaussian density estimation. It includes 
    an automatic centroids selection and parameter optimization algorithm, which 
    finds the number of clusters and cluster centroids by comparing average 
    silhouettes on a grid of testing clustering results; It also includes a user 
    interactive algorithm that allows the user to manually selects cluster 
    centroids from a two dimensional "density-distance plot". Here is the 
    research article associated with this package: "Wang, Xiao-Feng, and 
    Yifan Xu (2015)<DOI:10.1177/0962280215609948> Fast clustering using adaptive 
    density peak detection." Statistical methods in medical research". url:
    http://smm.sagepub.com/content/early/2015/10/15/0962280215609948.abstract.