This is an R package implementing the epidemic volatility index (EVI), as 
    discussed by Kostoulas et. al. (2021) and variations by Pateras et. al. (2023). EVI is a new, conceptually simple, early warning tool for oncoming epidemic waves. 
    EVI is based on the volatility of newly reported cases per unit of time,
    ideally per day, and issues an early warning when the volatility change rate exceeds a threshold.

Provides tools for integrated sensitivity analysis of evidence factors in observational
	studies. When an observational study allows for multiple independent or nearly
	independent inferences which, if vulnerable, are vulnerable to different biases, we have 
	multiple evidence factors.  This package provides methods that respect type I error rate control. 
	Examples are provided of integrated evidence factors analysis in a longitudinal study with
	continuous outcome and in a case-control study. 
	Karmakar, B., French, B., and Small, D. S. (2019)<DOI:10.1093/biomet/asz003>.

Implements likelihood-based evidence ratios for unified reporting
    in classical statistical testing. The package reports effect estimates,
    uncertainty intervals, and likelihood ratios on the log 10 scale derived
    from a single statistical model. It applies to standard normal mean tests,
    contingency tables, and regression coefficients, and provides a direct
    evidential measure while retaining classical error guarantees. 
    For the Evidence Ratio Reporting Standard see Lawless (2026) 
    <doi:10.5281/zenodo.18261076>.

Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data. 
  Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.

Contains a collection of examples of evidence factors in observational studies from the book Replication and Evidence Factors in Observational Studies by Paul R. Rosenbaum (2021) <doi:10.1201/9781003039648>.