History | Working with a real GEDCOM file | Analyzing the pedigree with BGmisc | Cleaning up | Summary of functions | A note on primary sources | References

What is a GEDCOM file? | Why use R for genealogy? | Exporting your own GEDCOM file | A minimal example | Reading individual records: readGedcom() | GEDCOM version | Quick overview: summarizeGedcom() | Working with dates | Parse to Date objects | Extract a year from any GEDCOM date string | Reading family records: readGedcomFamilies() | Working with coordinates | From GEDCOM to pedigree analysis | From GEDCOM to pedigree diagrams | Cleaning up | Summary of functions

Introduction | Basic usage | Customizing Aesthetics | Labeling Individuals | Overlaying Status Information | Highlighting Specific Individuals | Highlighting Harry Potter | Multiple families in one graphic | Adjusting Plot Appearance | Advanced Examples: Large Pedigrees with self-loops

Introduction | Prerequisites | Data Requirements at a Glance | Pedigree data frame | Relatedness matrices | Phenotype data format | Missing phenotypes | Part 1: Single-Family Model | Step 1: Subset to one family and inspect | Step 2: Compute relatedness matrices | Step 3: Prepare the phenotype data | Step 4: Subset matrices to observed individuals | Step 5: Check model identification | Step 6: Build and fit the model | Part 2: Two-Family Multi-Group Model | Family 2: matrices | Family 2: phenotype data | Family 2: subset matrices | Check identification across both families | Build and fit the two-family model | Part 3: Scaling Up — Many Families and Parameter Recovery | Simulate pedigrees and data | Build and fit the multi-family model | Compare estimates to true values | Using the high-level wrapper | Understanding the Covariance Structure | Summary

Introduction | Summarizing Pedigrees | Plotting Pedigree | Single Pedigree Visualization | Visualizing Multiple Pedigrees Side-by-Side

Introduction | Relatedness Coefficient | Inferring r from Observed Phenotypic Correlation | Computing Relatedness from Pedigree Data | Hazard Data Example | Introducing Missingness: Remove a Parent | Removing the Father Instead | Inbreeding Dataset: Family-Level Evaluation | Example: Family r famIDs[1] | Summary

Introduction | Loading Required Libraries | Working with Variance Component Models | Using comp2vech Function | Using identifyComponentModel Function | Extended Pedigrees

Introduction | Example data | Basic Usage

Introduction | Data Preparation | Calculating Relatedness Matrices | Visualizing Relatedness Matrices | Plot Interactively | Customization Options | Comparison to Base R and Other Approaches | Conclusions

Overview | Data Preparation | Data Cleaning | Load the data | Get kinship links for individuals with the following variables: | Create the df_flu_modeling object with only consistent responders. | Clean the column names with the {janitor} package. | Setting Up Color Palette | Plotting the Results | Individual Level Plot | Family Level Plots | Between Family Plots | Adding Marginal Density Plots | Assembling the Final Plot | Bonus: Faceting by SES Difference Group | Within Family Plots | Conclusion

Introduction | Load Packages and Data | Prepare and Validate Sex Codes | Compute Relatedness Matrices | Convert to Pairwise Format | Locate Jon and Daenerys | Plotting the Pedigree with Incomplete Parental Information | Visualize the Pedigree | Conclusion

Introduction | Loading Required Libraries and Data | Finding Extended Families | Computing Relatedness | Other relatedness measures | Computing mitochondrial relatedness | Computing relatedness through common nuclear environment | Computing relatedness through common extended family environment | Subsetting Pedigrees

Basic usage of config in ggPedigree() | Sex coding: code_male and code_female | Advanced Examples

Introduction | Data Structures and Preparation | Wide Format Data | Long Format Data | Converting Long to Wide | Using existing kinship links | Pedigree Data | Computing Kinship Matrices | Creating Wide-Format Kinship Pairs | Simulating Outcome and Predictor Variables | Ordering and Derived Variables | Understanding the Transformation | Data Selection for Standard OLS Regression | Analyzing the Data | Standard OLS Regression | Discordant Between-Family Regression | Discordant-Kinship Regression | Using the Helper Function | Comparing the Three Approaches | Side-by-Side Model Comparison | Interpreting the Differences | Conclusion | Session Info

Introduction | Loading Packages and Data | Constructing Kinship Links | Simulate Phenotypic Data | Fitting a Discordant-Kinship Regression Model | Alternative Specifications | Conclusion | References

Introduction | Identifying and Repairing ID Issues | A Clean Dataset | What checkIDs() Reports | A Tale of Two Duplicates | Repairing Between-Row Duplicates | Oedipus ID | Identifying and Repairing Sex Coding Issues | Best Practices for Pedigree Validation

Introduction | Purpose | Understanding Variable Types in Dyadic Analysis | Coding Approaches for Categorical Variables | Binary-match Coding | Multi-match Coding | Data Preparation | Package Loading and Data Setup | Handling Categorical Predictors | Mixed Variables: Sex as an Example | Between-dyad Variable: Race as an Example | Combining Binary and Multi-match Variables | Results and Interpretation | Regression Analysis: Sex Variables | Binary Match Coding for Sex | Interpretation: | Multi-Match Coding for Sex | Mean SES Model with Sex | Regression Analysis: Race Variables | Multimatch | Mean SES Model with Race | Regression Analysis: Combined Sex and Race Variables | Interpretation | Alternative Model: Binary Sex Match and Multi-Match Race | Mean SES Models with Both Variables | Mean SES Models with Combining multimatch and binarymatch | Conclusion | Implementation Recommendations: | References

Introduction | Data Description | Data Cleaning | Prepare person-level data | Create kinship-linked data | Modeling and Interpretation | Conclusion | Acknowledgments | Session Info | References

Components | Historical Archives | Support and Code Collaboration

Terminology | Example: DF analysis with a Simple Outcome for Gen2 Subjects, Using a Package Variable | Example: DF analysis with a univariate outcome from a Gen2 Extract | Example: Multiple Group SEM of a Simple Outcome for Gen2 Subjects | Example: Multiple Group SEM of a Simple Outcome for Gen1 Subjects | Example: Multiple Group SEM of a Simple Outcome all pairs in Gen1 and Gen2 | Appendix: Receiving Help for the NlsyLinks Package | Appendix: Creating and Saving R Scripts | Appendix: Installing and Loading the NlsyLinks Package | Appendix: References | Funding | Version Information

Investigators | Vanderbilt University, Peabody College of Education, Department of Psychology and Human Development | OUHSC Pediatrics | Howard Live Oak | NLSY Administrators | Bureau of Labor Statistics (BLS) | National Institute of Child Health and Human Development (NICHD) | Center for Human Resources Research (CHRR) | National Opinion Research Center (NORC) | Funding

Getting Started | Gen1 and Gen2 | Ambiguous twins | Ambiguous siblings | Retaining vs. dropping the ambiguous twins and siblings | Race and Gender Variables | Funding

Terminology | Steps to Select Variables and Download Data | Using Multiple Tagsets | Tagset History | Notes

Articles and Book Chapters | 2011 - Present | 2001 - 2010 | 1994 - 2000 | Recent Presentations by the Team | General NLSY Articles | Current Documentation, Vignettes, and User Support | Software Packages

NlsyLinks and Examples for R | Examples for SAS | NlsyLinksDetermination in C# | Support Forums

Previous dissemination mechanisms | Recent dissemination mechanisms | User-Service function | Vignettes and example documentation | Future plans