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.

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.

Introduction This blog is a new function, treatment_model that have been added to the Dyn4cast package. The function provides means for enhanced estimation of propensity score and treatments effects from randomized controlled designed experiments. Observational study involves the evaluation of outcomes of participants not randomly assigned treatments or exposures. To be able to assess the effects of the outcome, the participants are matched using propensity scores (PSM). This then enables the determination of the effects of the treatments on those treated against those who were not treated.