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 This blog is about the improved function, quicksummary in the Dyn4cast package. The function provides quick overview of data and particularly outputting five different means. Observational study involves procuring large mass of data for analysis and modeling. So, there is always need to have an overview of the data to decide on the appropriate analysis to be undertaken. This is where this function is unique because five different means are computed simultaneously, in spite of the one line code arguments.