Clean and Standardize Epidemiological Data

Cleaning and standardizing tabular data package, tailored specifically for curating epidemiological data. It streamlines various data cleaning tasks that are typically expected when working with datasets in epidemiology. It returns the processed data in the same format, and generates a comprehensive report detailing the outcomes of each cleaning task.

A Tidy Solution for Epidemiological Data

Offers a tidy solution for epidemiological data. It houses a range of functions for epidemiologists and public health data wizards for data management and cleaning.

A Toolbox for Public Health and Epidemiology

A toolbox for making R functions and capabilities more accessible to students and professionals from Epidemiology and Public Health related disciplines. Includes a function to report coefficients and confidence intervals from models using robust standard errors (when available), functions that expand 'ggplot2' plots and functions relevant for introductory papers in Epidemiology or Public Health. Please note that use of the provided data sets is for educational purposes only.

Genetic Epidemiology Design and Inference

Package for Genetic Epidemiologic Methods Developed at MSKCC. It contains functions to calculate haplotype specific odds ratio and the power of two stage design for GWAS studies.

Plausibility Analysis of Epidemiological Signals

Provides functionality to prepare data and analyze plausibility of both forecasted and reported epidemiological signals. The functions implement a set of plausibility algorithms that are agnostic to geographic and time resolutions and are calculated independently then presented as a combined score.

UK Epidemiological Data Management

Contains utilities and functions for the cleaning, processing and management of patient level public health data for surveillance and analysis held by the UK Health Security Agency, UKHSA.

Flexible Contingency Tables for Epidemiology

Builds contingency tables that cross-tabulate multiple categorical variables and also calculates various summary measures. Export to a variety of formats is supported, including: 'HTML', 'LaTeX', and 'Excel'.

Data Quality in Epidemiological Research

Data quality assessments guided by a 'data quality framework introduced by Schmidt and colleagues, 2021' target the data quality dimensions integrity, completeness, consistency, and accuracy. The scope of applicable functions rests on the availability of extensive metadata which can be provided in spreadsheet tables. Either standardized (e.g. as 'html5' reports) or individually tailored reports can be generated. For an introduction into the specification of corresponding metadata, please refer to the 'package website' < https://dataquality.qihs.uni-greifswald.de/VIN_Annotation_of_Metadata.html>.

Combined Visualisation of Phylogenetic and Epidemiological Data

A collection of utilities and 'ggplot2' extensions to assist with visualisations in genomic epidemiology. This includes the 'phylepic' chart, a visual combination of a phylogenetic tree and a matched epidemic curve. The included 'ggplot2' extensions such as date axes binned by week are relevant for other applications in epidemiology and beyond. The approach is described in Suster et al. (2024) .

Basic Sensitivity Analysis of Epidemiological Results

Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Lash T.L, Fox M.P, and Fink A.K. "Applying Quantitative Bias Analysis to Epidemiologic Data", ('Springer', 2021).