Increasing the Lifespan of Software for Demographic Analysis

Many researchers face challenges with computational reproducibility. For instance, running analysis code written just a year earlier can be problematic. Even if it worked flawlessly and gave the expected results earlier, it might fail due to errors now (see Fig. 1). These issues are typically due to the use of newer versions of analysis software…. Read More

Expanding the Lifespan of Software for Demographic Analysis with Containers: An Application of Spatial Sampling

Introduction Software, such as specific R packages, evolve over time, which may prevent older analysis code from working as expected. For example, default values for arguments in a function can change. Therefore, for computational reproducibility, knowing which specific R and package versions were used to run the analysis is crucial. One popular solution in R… Read More