Introduction | Related packages | Installation | A self-contained example | Running mgcca | Individuals plot | Variables plot | Variance explained | Significant variables | The two plotting families | Saving and reloading results | How it scales to omics data | A live multi-omics example: miniACC | Assembling the tables | Choosing the penalty | Integrating the two layers | Reading the components | Are the components themselves significant? | Scoring individuals the fit never saw | Auditing the availability design before fitting | Auditing the object you are about to fit | Appendix: the full-scale TCGA walkthrough (not evaluated) | Download, impute and assemble the tables | Integrate the two layers | Do the shared components track the disease? | What drives the components | Subsetting by genomic ranges | Session information

Introduction | Reading the numbers without the algebra | Two words used throughout | What "alignment" actually means | The four numbers | In what order to look at things | What each combination means | Installation | A self-contained example | Does the omic signal know anything about my variable? | Which data type is the finding coming from? | When your study has batches, centres or stages | Is my result leaning on those groups? | Seeing it instead of inferring it | Which participants is it coming from? | Would I get the same answer with a different sample? | A result you can lean on | A result you cannot | What made the difference | Practical matters | Where the computation happens | Keeping your results | What you can and cannot claim | Session information

Introduction | Background | Methodology | Setup | Installing the packages | Loading libraries | Quick start | Datasets | IDAT files and reference panel | Candidate regions | Preprocessing | Epimutations | Epimutations detection | Unique parameters | Results description | Epimutations annotations | Epimutation visualization | Acknowledgements | Session Info | References

Introduction | Quick start | Which clocks are available, and what they are for | Chronological-age clocks | Biological-age clocks | Gestational-age clocks | Mitotic (cell-division) clocks | Pace of ageing, and other measures | Trait and exposure predictors | Getting started | What data to provide | Missing values and imputation | Running the clocks and reading the output | Seeing and interpreting the results | Predicted versus real age | Do the clocks agree with each other? | How the estimates are distributed | Age acceleration | Cell composition and immune-aware acceleration | Comparing groups | Shapes: densities by group | Trends: fitted lines by group | Summary: the difference, clock by clock | Quality control: does a clock fit your data? | Reference ranges: your samples against a named cohort | Choosing the right clock for your data | Longitudinal data: trajectories and slopes | Beyond age: a trait predictor | EPICv2 arrays | Session info | References

Introduction | The data | Estimating the survival of the gap times | Comparing the three estimators | Groups and covariates | Comparing survival quantiles between groups | Session information | References

Introduction | The model | Installation | Quick start | Input data | Adding missing censoring times | Other input formats | Exploring the data | Event chart | Mean cumulative function | Fitting the model | Arguments | Interpreting the output | Testing model terms | Baseline functions | Predictions for covariate profiles | Effective age models | Effective age from treatment response | Frailty models | Standard errors | Migrating from version 1.x | Session info | References

Introduction | Data loading | Descriptive analysis | Hardy-Weinberg equilibrium | SNP association analysis | Gene-environment and gene-gene interactions | Haplotype analysis | Haplotype estimation | Haplotype association | Sliding window approach | Session info | References

Overview | sessionInfo()

Getting started | Normalization and filtering | The Poisson-Tweedie family of distributions to model RNA-seq count data | Goodness of fit to a count data distribution | Testing for differential expression | Visualizing the results | Assessing differential expression calling accuracy | Fitting generalized linear models (GLM) | Session info

Overview | Chronological DNAm age (in years) | Gestational DNAm age (in weeks) | How to load data | sessionInfo()