Directed Acyclic Graphs: Analysis and Data Simulation

Draw, manipulate, and evaluate directed acyclic graphs and simulate corresponding data, as described in International Journal of Epidemiology 50(6):1772-1777.

Supporting Functions Maintained by Zhen Lu

Miscellaneous functions commonly used by LuLab. This package aims to help more researchers on epidemiology to perform data management and visualization more efficiently.

Multiple Mediation Analysis

Used for general multiple mediation analysis. The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.

The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data in Sri Lanka

Provides a daily counts of the Coronavirus (COVID19) cases by districts and country. Data source: Epidemiological Unit, Ministry of Health, Sri Lanka < https://www.epid.gov.lk/web/>.

Converting Weekly Data to Monthly Data

Converts weekly data to monthly data. Users can use three types of week formats: ISO week, epidemiology week (epi week) and calendar date.

Optimal Channel Networks

Generate and analyze Optimal Channel Networks (OCNs): oriented spanning trees reproducing all scaling features characteristic of real, natural river networks. As such, they can be used in a variety of numerical experiments in the fields of hydrology, ecology and epidemiology. See Carraro et al. (2020) for a presentation of the package; Rinaldo et al. (2014) for a theoretical overview on the OCN concept; Furrer and Sain (2010) for the construct used.

Functions to Interact with the 'FAIR Data Pipeline'

R implementation of the 'FAIR Data Pipeline API'. The 'FAIR Data Pipeline' is intended to enable tracking of provenance of FAIR (findable, accessible and interoperable) data used in epidemiological modelling.

Prepare, Analyze, and Visualize Covid-19 Wastewater Data

Intended to make the process of analyzing epidemiological wastewater data easier and more insightful. Includes tools for preparing, analyzing, and visualizing data. It additionally includes Wisconsin's Covid19 data.

Combining Different Spatial Datasets in Cancer Risk Estimation

We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. See Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford, Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical Association, 109:505, 11-23, .

A 'shiny' Wrapper of the R Package 'epiworldR'

R 'shiny' web apps for epidemiological Agent-Based Models. It provides a user-friendly interface to the Agent-Based Modeling (ABM) R package 'epiworldR' (Meyer et al., 2023) . Some of the main features of the package include the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Recovered (SIR), and Susceptible-Exposed-Infected-Recovered (SEIR) models. 'epiworldRShiny' provides a web-based user interface for running various epidemiological ABMs, simulating interventions, and visualizing results interactively.