Calculates a cumulative summation nonparametric extended 
    median test based on the work of Brown & Schaffer (2020) <DOI:10.1080/03610926.2020.1738492>. 
    It then generates a control chart to assess processes and determine if any streams are out of control.

The aim of neo2R is to provide simple and low level connectors
   for querying neo4j graph databases (<https://neo4j.com/>).
   The objects returned by the query functions are either lists or data.frames
   with very little post-processing.
   It allows fast processing of queries returning many records.
   And it let the users handle post-processing according to the data model
   and their needs.

Sends queries to a specified 'Neo4J' graph database, capturing results in a dataframe where appropriate.  
    Other useful functions for the importing and management of data on the 'Neo4J' server and basic local server admin.

A Modern and Flexible 'Neo4J' Driver, allowing you to query
    data on a 'Neo4J' server and handle the results in R. It's modern in
    the sense it provides a driver
    that can be easily integrated in a data analysis workflow, especially by
    providing an API working smoothly with other data analysis and graph
    packages. It's flexible in the  way it returns the results, by
    trying to stay as close as
    possible to the way 'Neo4J' returns data. That way, you have the control
    over the way you will compute the results. At the same time, the result
    is not too complex, so that the "heavy lifting" of data wrangling is not
    left to the user.

Calculating the density, cumulative distribution, quantile, and random number of
    neo-normal distribution. It also interfaces with the 'brms' package,
    allowing the use of the neo-normal distribution as a custom family.
    This integration enables the application of various 'brms' formulas
    for neo-normal regression.  Modified to be Stable as Normal from Burr (MSNBurr), 
    Modified to be Stable as Normal from Burr-IIa (MSNBurr-IIa), Generalized of MSNBurr (GMSNBurr), 
    Jones-Faddy Skew-t, Fernandez-Osiewalski-Steel Skew Exponential Power,  
    and Jones Skew Exponential Power distributions are supported.
    References: Choir, A. S. (2020).Unpublished Dissertation, Iriawan, N. (2000).Unpublished Dissertation,
    Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z., & Bastiani, F. D. (2019) <doi:10.1201/9780429298547>.