Implementation of frequency tables and bar charts for qualitative variables and checkbox fields. This package implements tables and charts used in reports at Funarte (National Arts Foundation) and OBEC (Culture and Creative Economy Observatory) in Brazil, and its main purpose is to simplify the use of R for people with a background in the humanities and arts. Examples and details can be viewed in this presentation from 2026: <https://formacao2026.netlify.app/assets/modulo_3/modulo3#/title-slide>.

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.

A comprehensive data validation package that allows comparing
    datasets using configurable validation rules defined in 'YAML' files.
    Built on top of the 'pointblank' package for robust data validation, it
    supports exact matching, tolerance-based numeric comparisons, text
    normalization, and row count validation.

Profiles R data frames as compact data fingerprints using schema,
    shape, missingness, distribution, category, uniqueness, time, and role
    signals. It compares versions, identifies close relatives in a library of
    historical data sets, and renders portable HTML cards plus static PNG/PDF
    lineage figures for reports.

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), Adaptive WINdowing
    (ADWIN) 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>,
    Bifet and Gavalda (2007) <doi:10.1137/1.9781611972771>, 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>.