Introduction Model tuning and estimation has evolved from simple extrapolation to sophisticated probabilistic modeling frameworks. In contemporary data science, decision-makers require more than estimates, the need for clear statements about likelihood, risk, and uncertainty are critical to correct decision-making. However, despite the advances in predictive modeling, there is a persistent limitation of making quick and robust decisions from the estimated probabilities. The estimates are often not meaningfully interpreted. This is where the odds_summary function becomes strategically important.

Introduction In today’s complex and dynamic world, understanding the stability and fragility of systems and users is crucial for ensuring resilience and mitigating potential risks. Resilience capacity and vulnerability index are important in measuring the ability of systems and users to withstand and recover from disruptions. Resilience Capacity Resilience capacity refers to the ability of systems and users to absorb and adapt to disturbances, maintaining their functionality and performance. Resilience capacity has three components (Fig.

Introduction The Dyn4cast function was launched purposely to facilitate fast and easy forecast of COVID-19 cases in Nigeria. It was converted to a package and the development version of the package was launched on the 17th of February, 2021 with a single function DynamicForecast. The tenet of this package is to present functions that are easy to implement and use. Over the last five years, additions continue to come up as the need arises and today, there are more than 10 functions that are up mostly in one line of codes.