1. Creating from R Data Structures | From a matrix | From a data frame | 2. Importing from Local Files | CXT Format (.cxt) | CSV Files (.csv) | 3. Fetching from the FCA Repository | Browsing the Repository | Downloading a Specific Context | 4. Interactive Use: The RStudio Addin | How to Launch It | What Does the Addin Do? | Summary of Methods

Introduction | 1. Boolean Matrix Factorization with GreConD | Dataset Creation | Performing Factorization | Interpreting the Factors | The Object-Factor Matrix ($A$) | The Factor-Attribute Matrix ($B$) | 2. Rice-Siff Factorization (RSF and RSF-ES) | RSF with Early Stopping (RSF-ES) | Performing RSF Factorization | 3. Other Algorithms: ASSO | Acknowledgments | References

Introduction | 1. Checking lattice properties | Understanding the properties | Example: A non-distributive lattice ($M_3$) | 2. Arrow Relations

Introduction | Computing Bonds | Computation Methods | The BondLattice Object | Visualization | Extracting Bonds | The Core Bond | Verifying Bonds | Similarity and Complexity Metrics | Order-Theoretic Properties | Summary

Introduction | Datasets | Working with Formal Contexts | Plotting, printing and latex-ing the FormalContext | Importing a Formal Context from File | Dual Formal Context | Closures | Clarification and Reduction | Extracting Implications and Concepts | Protoconcepts | Standard Context | Saving and loading | Concept Lattice | Plot, print and LaTeX | Getting all extents, intents and retrieving concepts | Concept support | Sublattices | Subconcepts, superconcepts, infimum and supremum | Join- and meet- irreducible elements

Introduction | Datasets | Extraction of the canonical basis of implications | Validity of implications | Cardinality, size and support of the implication set | Export to LaTeX | Filtering of implications | Simplification Logic | Entailment and equivalence of implications | Standard Context | Recommendation systems

Setup | FormalContext | Creating and Exporting | Importing | Recursive Export | ConceptLattice | ImplicationSet | RuleSet | Summary

Introduction and Motivation | Types of Scaling | Nominal scaling | Ordinal scaling | Interordinal scaling | Biordinal scaling | Interval scaling | Scaling in fcaR | Available scales | Applying scales | Scale contexts | Background knowledge | Concepts and implications | Another example

Introduction | The Approach | Example 1: Direct Causality | Example 2: Simpson's Paradox (Spurious Correlation) | Conclusion

Introduction | Setup | Part 1: Context Manipulation | 1.1 Renaming and Feature Engineering | 1.2 Filtering and Selecting | Part 2: Mining and Filtering Implications | 2.1 Filtering by Metrics | 2.2 Semantic Filtering | 2.3 Complex Pipelines | Conclusion

Introduction | 1. Dirichlet Distribution for Realistic Data | 2. Randomization via Edge Swapping | 3. Generating Distributive Lattices

Introduction | Stability | Separation | Fuzzy density | Putting it all together

Introduction | 1. Creating a fuzzy formal context | 2. Fuzzy logics and operators | Available logics | Changing the logic | 3. Mining fuzzy concepts | 4. Visualization | 5. Fuzzy implications | Support and confidence | 6. Fuzzy closure and recommendation | Summary of formulas

1. Standard visualization | Layout algorithms | 1. Sugiyama layout (layered) | 2. Force-directed layout (organic) | 2. Labeling modes | Reduced labeling | Full labeling | Empty (structure only) | 3. Exporting to LaTeX (TikZ) | Saving to a file | How to use it in LaTeX

Introduction | The Registry | Use of the Rules | Definition of New Equivalence Rules | An Example

Introduction | Datasets | Converting between formal contexts and transactions objects | Importing a transactions object | Exporting a FormalContext | Converting between implication sets and rules objects | Importing rules as an implication set | Exporting ImplicationSets to rules format | Final considerations