Implementation of several different methods for estimating a graphs max degree by only visiting a faction of the nodes. Different estimators are implemented as subclasses of a base estimator class that provides iterative updates of sample mean, sample variance, and confidence interval. Experiments are defined via: 1. the type of random graph to generate 2. the number of random graphs to generate 3. where to save the results 4. what type of estimators to use 5. the different sampling budgets to use Random graphs are provided through builder functions that accept the size of the graph and returns a function for generating the random samples.