1. Introduction | 1.1. Installation | 2. Necessary Data | 2.1. Time scales | 2.2. Occurrence table | 2.2.1. Stratigraphic assignment | 2.2.2. The example file | 3. Basic patterns | 3.1. Ranges | 3.2. Sampling parameters | 3.2.1. Basic descriptors | 3.2.2. Plotting of counts and proportions | 3.2.3. Single-collection and single reference taxa | 4. Raw diversity dynamics | 4.1. Diversity (taxonomic richness) with discrete time input | 4.1.1.Taxon counts | 4.1.2. Different metrics of richness | 4.2. Taxonomic rates with discrete time input | 4.2.1. Different metrics of turnover | 4.2.2. Selectivity testing for the per capita rates | 4.3. Diversity dynamics with age input | 4.3.1. Using the bin argument – unique values code intervals | 4.3.2. Slicing: slice() and the age argument of divDyn() | 4.3.3. Summary – about slicing and the direction of time | 4.4. Origination and extinction dates in binary response variables | 5. Sampling standardization | 5.1. Concepts | 5.1.1. The goal of sampling standardization | 5.1.2. The formal representation of subsampling | 5.2. Basic arguments | 5.2.1. Subsampling level | 5.2.2. The number of iterations/trials | 5.2.3. The duplicates argument | 5.2.4. The useFailed argument | 5.2.5. The keep and rem arguments | 5.2.6. Subsampling ‘output’ and plotting options | 5.3. The applied function during the subsampling | 5.3.1. Example 1: Checking the number of occurrences | 5.3.2. Example 2: maximum absolute paleolatitudes | 5.4. Different subsampling types | 5.4.1. Classical Rarefaction (CR) | 5.4.2. Occurrence-weighted by-list subsampling (O^x^W) | 5.4.3. Shareholder Quorum Subsampling (SQS) | 5.4.3.1. The ‘inexact’ approach to SQS | 5.4.3.2. Concluding remarks on SQS | 5.4.4. Subsampling trial functions | 6. Taxon-specific characteristics | 6.1. Environmental affinities | 6.1.1. Majority rule | 6.1.2. Binomial method | 6.2. Changing characteristics | 6.2.1. Geographic range | 6.2.2. The tabinate() iterator function | Acknowledgements | References