Supports the process of applying a cut to Standard Data Tabulation Model (SDTM),
    as part of the analysis of specific points in time of the data, normally as part of 
    investigation into clinical trials. The functions support different approaches of
    cutting to the different domains of SDTM normally observed.

Creates a data dictionary from any dataframe or tibble in your R environment. 
    You can opt to add variable labels. You can write the object directly to Excel.

Query for metrics from 'Datadog' (<https://www.datadoghq.com/>) via its API.

A system designed for detecting concept drift in streaming datasets. It offers a comprehensive suite of statistical methods to detect concept drift, including methods for monitoring changes in data distributions
    over time. The package supports several tests, such as Drift Detection Method (DDM), Early Drift Detection Method (EDDM), Hoeffding Drift Detection Methods (HDDM_A, HDDM_W), Kolmogorov-Smirnov test-based Windowing (KSWIN)
    and Page Hinkley (PH) tests. The methods implemented in this package are based on established research and have been demonstrated to be effective in real-time data analysis. For more details on the methods, 
    please check to the following sources. Kobylińska et al. (2023) <doi:10.48550/arXiv.2308.11446>, S. Kullback & R.A. Leibler (1951) <doi:10.1214/aoms/1177729694>, Gama et al. (2004) <doi:10.1007/978-3-540-28645-5_29>, Baena-Garcia et al. (2006) <https://www.researchgate.net/publication/245999704_Early_Drift_Detection_Method>,
    Frías-Blanco et al. (2014) <https://ieeexplore.ieee.org/document/6871418>, Raab et al. (2020) <doi:10.1016/j.neucom.2019.11.111>, Page (1954) <doi:10.1093/biomet/41.1-2.100>, Montiel et al. (2018) <https://jmlr.org/papers/volume19/18-251/18-251.pdf>.

An interactive editor built on 'rhandsontable' to allow the 
  interactive viewing, entering, filtering and editing of data in R 
  <https://dillonhammill.github.io/DataEditR/>.

Automated data exploration process for analytic tasks and predictive modeling, so
    that users could focus on understanding data and extracting insights. The package scans and
    analyzes each variable, and visualizes them with typical graphical techniques. Common
    data processing methods are also available to treat and format data.