Tools and functions for parsing, rendering and operating on
    semantic version strings. Semantic versioning is a simple set of rules
    and requirements that dictate how version numbers are assigned and
    incremented as outlined at <https://semver.org>.

Provides functions to access and collect data from the Brazilian
    Federal Senate open data API and website. Covers senators, legislative
    materials, committees, voting records, speeches, provisional measures,
    vetoes, and legislative agendas, returning results as tidy data frames
    ready for analysis.

Send email using 'Sendgrid' <https://sendgrid.com/> 
            mail API(v3) <https://docs.sendgrid.com/api-reference/how-to-use-the-sendgrid-v3-api/authentication>.

A system enables cross study Analysis by extracting and filtering study data for 
    control animals from  'CDISC' 'SEND' Study Repository. 
    These data types are supported: Body Weights, Laboratory test results and Microscopic findings. 
    These database types are supported: 'SQLite' and 'Oracle'.

Package contains a simple SMTP client with minimal dependencies which 
        provides a portable solution for sending email, including file attachments and inline html reports, 
        from within R. SMTP Authentication and SSL/STARTTLS is implemented using curl.

Stacked ensemble for regression tasks based on 'mlr3' framework with a pipeline for preprocessing numeric and factor features and hyper-parameter tuning using grid or random search.

Implements a suite of sensitivity analysis tools 
  that extends the traditional omitted variable bias framework and makes it easier 
  to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.

Provides statistical diagnostics to evaluate whether proxy indicators reliably represent an unobservable target construct. The main function 'senser()' assesses proxies across multiple dimensions including monotonicity, information content, stability, distributional alignment, and potential bias risk. It prints a concise, interpretable summary suitable for analytical and policy-oriented assessment, without claiming causal inference.