Efficient Estimation of Clustered Current Status Data

Current status data abounds in the field of epidemiology and public health, where the only observable data for a subject is the random inspection time and the event status at inspection. Motivated by such a current status data from a periodontal study where data are inherently clustered, we propose a unified methodology to analyze such complex data.

Work with Two-by-Two Tables

A collection of functions for data analysis with two-by-two contingency tables. The package provides tools to compute measures of effect (odds ratio, risk ratio, and risk difference), calculate impact numbers and attributable fractions, and perform hypothesis testing. Statistical analysis methods are oriented towards epidemiological investigation of relationships between exposures and outcomes.

Predicting Disease Spread from Flow Data

Provides functions and classes designed to handle and visualise epidemiological flows between locations. Also contains a statistical method for predicting disease spread from flow data initially described in Dorigatti et al. (2017) . This package is part of the RECON (< https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.

Clinical Indices and Outcomes Tools

Collection of indices and tools relating to cardiovascular, nephrology, and hepatic research that aid epidemiological chort or retrospective chart review with big data. All indices and tools take commonly used lab values and patient demographics and measurements to compute various risk and predictive values for survival. References to original literature and validation contained in each function documentation.

Working with Healthcare Databases

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) ) directly on a database server. The implementation is based on 'dbplyr' with full 'tidyverse' compatibility.

Implement the AMIS Algorithm for Infectious Disease Models

Implements the Adaptive Multiple Importance Sampling (AMIS) algorithm, as described by Retkute et al. (2021, ), to estimate key epidemiological parameters by combining outputs from a geostatistical model of infectious diseases (such as prevalence, incidence, or relative risk) with a disease transmission model. Utilising the resulting posterior distributions, the package enables forward projections at the local level.

Analysis of Virulence

Epidemiological population dynamics models traditionally define a pathogen's virulence as the increase in the per capita rate of mortality of infected hosts due to infection. This package provides functions allowing virulence to be estimated by maximum likelihood techniques. The approach is based on the analysis of relative survival comparing survival in matching cohorts of infected vs. uninfected hosts (Agnew 2019) .

Causes of Outcome Learning

Implementing the computational phase of the Causes of Outcome Learning approach as described in Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. 2022. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology . The optional 'ggtree' package can be obtained through Bioconductor.

A 'Shiny' App to Simulate the Dynamics of Epidemic and Endemic Diseases Spread

The 'EpiSimR' package provides an interactive 'shiny' app based on deterministic compartmental mathematical modeling for simulating and visualizing the dynamics of epidemic and endemic disease spread. It allows users to explore various intervention strategies, including vaccination and isolation, by adjusting key epidemiological parameters. The methodology follows the approach described by Brauer (2008) . Thanks to 'shiny' package.

Bayesian Analysis of Epidemic Data Using Line List and Case Count Approaches

Provides tools for performing Bayesian inference on epidemiological data to estimate the time-varying reproductive number and other related metrics. These methods were published in Li and White (2021) . This package supports analyses based on aggregated case count data and individual line list data, facilitating enhanced surveillance and intervention planning for infectious diseases like COVID-19.