Contains the Gene ontology terms and skeleton for the reduced GO directed acyclic graph (DAG) for the organisms
             Rat and Mouse. The methods are explicitly discussed in the following article : Manjang et al (2020) <doi:10.1038/s41598-020-73326-3>.

Fit Good regression models to count data (Tur et al., 2021) <doi:10.48550/arXiv.2105.01557>. The package provides functions for model estimation and model prediction. Density, distribution function, quantile function and random generation for the Good distribution are also provided.

Reconstruction of muscle fibers from image stacks using textural analysis. Includes functions for tracking, smoothing, cleaning, plotting and exporting muscle fibers. Also calculates basic fiber properties (e.g., length and curvature).

Performing goodness-of-fit tests for stochastic block models used to fit network data. Among the three variants discussed in Karwa et al. (2023) <doi:10.1093/jrsssb/qkad084>, goodness-of-fit test has been performed for the Erdos-Renyi (ER) and Beta versions.

Association analysis between categorical
    variables using the Goodman and Kruskal tau measure. This asymmetric association
    measure allows the detection of asymmetric relations between categorical
    variables (e.g., one variable obtained by re-grouping another).

Give advice about good practices when building R packages.
    Advice includes functions and syntax to avoid, package structure, code
    complexity, code formatting, etc.

Collect  marketing data from 'Google Ads' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

Interact with the Google Analytics 
  APIs <https://developers.google.com/analytics/>, including 
  the Core Reporting API (v3 and v4), Management API, User Activity API
  GA4's Data API and Admin API and Multi-Channel Funnel API.

Create R functions that interact with OAuth2 Google APIs 
    <https://developers.google.com/apis-explorer/> easily,
    with auto-refresh and Shiny compatibility.

Tools to easily enable R scripts in the Google Cloud Platform.
  Utilise cloud services such as Cloud Run <https://cloud.google.com/run/> for R 
  over HTTP, Cloud Build <https://cloud.google.com/build> for Continuous 
  Delivery and Integration services and 
  Cloud Scheduler <https://cloud.google.com/scheduler/> for scheduled scripts.