A system for identifying diseases or events from healthcare databases and
    preparing data for epidemiological studies. It includes capabilities not
    supported by 'SQL', such as matching strings by 'stringr' style regular
    expressions, and can compute comorbidity scores (Quan et al. (2005)
    <doi:10.1097/01.mlr.0000182534.19832.83>) directly on a database server. The
    implementation is based on 'dbplyr' with full 'tidyverse' compatibility.

Compute 21 summary measures of health inequality and its
    corresponding confidence intervals for ordered and non-ordered
    dimensions using disaggregated data. Measures for ordered dimensions
    (e.g., Slope Index of Inequality, Absolute Concentration Index) also
    accept individual and survey data.

Provides a shiny interface for a free, open-source managerial
    accounting-like system for health care practices. This package allows
    health care administrators to project revenue with monthly adjustments
    and procedure-specific boosts up to a 3-year period. Granular data
    (patient-level) to aggregated data (department- or hospital-level) can
    all be used as valid inputs provided historical volume and revenue
    data is available. For more details on managerial accounting
    techniques, see Brewer et al. (2015, ISBN:9780078025792).

Provides a broad collection of datasets focused on health, biomechanics, and human motion.
    It includes clinical, physiological, and kinematic information from diverse sources,
    covering aspects such as surgery outcomes, vital signs, rheumatoid arthritis, osteoarthritis,
    accelerometry, gait analysis, motion sensing, and biomechanics experiments.
    Designed for researchers, analysts, and students, the package facilitates exploration
    and analysis of data related to health monitoring, physical activity, and rehabilitation.

Efficient tools for parsing and standardizing Australian 
    addresses from textual data. It utilizes optimized algorithms to accurately identify and 
    extract components of addresses, such as street names, types, and postcodes, especially  
    for large batched data in contexts where sending addresses to internet services may be 
    slow or inappropriate. The core functionality is built on fast string processing techniques 
    to handle variations in address formats and abbreviations commonly found in Australian 
    address data. Designed for data scientists, urban planners, and logistics analysts, the 
    package facilitates the cleaning and normalization of address information, supporting 
    better data integration and analysis in urban studies, geography, and related fields.