Continuous Data | Example 1 | New Tests | Adjusted p values | Power Estimation | Example 2 | Discrete Data | Example 3 | User supplied tests | Goodness-of-fit/Two-sample hybrid problem | Benchmarking | Goodness-of-fit / twosample hybrid problem. | Example 5 | Methods included in MD2sample for continuous data | Methods whose p values are found via simulation | Methods based on empirical distribution functions | Methods based on nearest neighbors | Methods based on distances between observations | Methods based on kernel density estimates | Methods whose p values are found using a large sample theory | Methods included in MD2sample for discrete data | References

Continuous Data, dim=2, equal marginals, no parameter estimation | Continuous Data, dim=2, unequal marginals, no parameter estimation | Continuous Data, dim=2, equal marginals, with parameter estimation | Continuous Data, dim=2, unequal marginals, with parameter estimation | Continuous Data, dim=5, equal marginals, no parameter estimation | Continuous Data, dim=5, unequal marginals, no parameter estimation | Discrete data

Example 1: Continuous data without parameter estimation | Example 2: Continuous data with parameter estimation | New Tests | Example 3 | Adjusted p values | Power Estimation | Example 4 | Discrete Data | Example 5 | Example 6 | User supplied tests | Example 7 | Benchmarking | Example 8

Example 1: Continuous data without parameter estimation | Example 2: Continuous data with parameter estimation | New Tests | Example 3 | Power Estimation | Example 4 | Discrete Data | Example 5 | User supplied tests | Example 7

Continuous data | Tests based on a comparison of the theoretical and the empirical distribution function. | Tests based on the Rosenblatt transform. | Discrete data | Methods based on the empirical distribution fuction. | Methods based on the density | References

Dim=2, equal marginals | Dim=2, unequal marginals | Dim=5, equal marginals | Dim=5, unequal marginals

Example 1 | New Tests | Arguments and output of new test routine for continuous data | Example 2 | New Test | Arguments and output of new test routine for discrete data | Permutation Method | Parametric Bootstrap | Example 3 | The Testing Methods | The Arguments of twosample_test | The Output of twosample_test | shiny app | twosample_power and plot_power | Example 4 | The Arguments of twosample_power | Example 5 | Example 6 | Adjusted p values for Several Tests | Case Studies | References

The Methods | Testing | Discrete (Histogram) Data/Model | Simple Null Hypothesis | Random Sample Size | Composite Null Hypothesis | Histogram Data | Continuous Data | Simple Hypothesis | Composite Hypothesis - One Parameter | Composite Hypothesis - Multiple Parameters | Power Estimation | Discrete Data/Model | Continuous Data/Model | Running other tests | Adjusted p values for Several Tests | Weighted Data | Case Studies | References

Basic Workflow | shinymoodlequizR | My First Quiz | Question / Part of Question and @ | R Scipts | CLOZE | Helper Routines | Creating the XML file | Multiple Stories | Data Sets | Graphics | Non Statistics Courses | Conclusions