Overview | Step 1: Bandpass filtering | Step 2: Instantaneous phases via the Hilbert transform | Step 3: Dynamic phase-locking matrix (dPL) and LEiDA | The dPL matrix | The leading eigenvector (LEiDA) | Step 4: Brain state discovery with K-means | Choosing K | State sequence | Step 5: Kuramoto order parameter | The measure | Metastability | References

Overview | Sliding-window correlation | Window length: a critical choice | Validating against static FC | FC variability across windows | Edge-centric cofluctuations | Root-sum-square (RSS) cofluctuation | Correlation of correlations | Brain state analysis from sliding-window FC | References

What is dynamic functional connectivity? | When to use which approach | Package data | Pipeline overview | Quick example: phase-based pipeline | Quick example: correlation-based pipeline | Sanity check: single window equals static FC | Further reading | References

Installation | Basic workflow | 1. Read fMRIPrep derivatives | 2. Prepare the BOLD object | 3. Load an atlas and parcellate | Custom atlas | 4. Compute metrics | 5. Export for syncR | Ecosystem

What is mrpheus? | Installation | Staging model | Reading a recording | Preparing a recording | Artefact detection | Spectral analysis | Band power | Spectrogram | Sleep event detection | Spindles | Slow oscillations | Automatic sleep staging | Exporting to hypnor | Full pipeline | References

Overview | Installation | Basic workflow | Supported input formats | Staging resolution

Overview | P-value style | Decimal places | Significance stars | FDR display | Domain labels for unassigned variables | Session-wide vs per-call options | Full option reference

Overview | Setup | 1. Simulate a family cohort | 2. Build GRM and run batch estimation | 3. Render the full table | Filtering to a single model | HTML output

Overview | Basic long-format table | Including sample size | Domain grouping | FDR correction | Wide (pivoted) format | Custom column names | HTML output

Overview | Basic usage | Group comparisons | Domain grouping | Log-transformed variables | FDR correction | Selecting variables | HTML output | Inspecting the underlying data

Overview | Installation | The example datasets | A minimal Table 1 | Adding domain grouping | Changing the output format | The clerkR palette | What next?

Overview | Basic usage | Domain grouping | FDR correction | Using broom::tidy() directly | Logistic regression — exponentiated coefficients | Custom column names | HTML output

Overview | Basic usage | Selecting variables | Domain grouping | Log-transformed variables | HTML output | When to use tbl_simple() vs tbl_descriptive()

diverging — complex | diverging_simple — simple | Side-by-side comparison | Controlling limits and midpoints

The main palette | The core palette | Reversing the palette | Retrieving colours directly

Complex sequential | blues | warm | Simple sequential | Combining with theme_circadia() | Reversing

Palettes | Domain colours | The theme_circadia() theme | Continuous scales

Overview | Test data | tallieR data | slumbR data | zeitR data | Synchronising with sync() | Default left join | Inner join | Full join | Custom participant ID column | Partial sync | Example analyses | Circadian type vs. sleep efficiency | Insomnia severity vs. objective sleep | Substance use and sleep quality | Nights with poor sleep efficiency | Working with real data | Session info

1. Read the recording | 2. Run the full pipeline | 3. Nightly sleep statistics | Timestamp issues | 4. Actogram | 5. Circadian rhythm analysis (NPCRA) | Next steps

What is zeitR? | Installation | Reading a single recording | The $epochs tibble | The $metadata list | Running the full pipeline | Nightly sleep statistics | Timestamp issues | Running pipeline steps individually | Next steps

Overview | The five variables | Interdaily stability (IS) | Intradaily variability (IV) | Relative amplitude (RA) | L5 and M10 | Computing NPCRA | Interpretation example | Customising window widths | Using raw data frames | Batch NPCRA across a study | References

Overview | Reading a directory of files | Participant-level summary | Column reference | Running the full pipeline across a study | Combining NPCRA and sleep statistics | Tips | Further reading

Overview | 1. Build the GRM | 2. Estimate heritability for a single trait | 3. Batch estimation | 4. Forest plot | 5. Colour palette | 6. Realistic workflow | 7. Citation

The JSON spec format | Supported scoring types | Key fields reference | Loading a custom instrument | Scoring with a custom instrument | Using custom instruments with imports | Loading multiple instruments | Composite scoring | Inspecting a loaded registry

Installation | Basic workflow | 1. Read an export | 2. Wide score table | 3. Long score table | 4. Item-level data | Scoring and interpretation | Available instruments | Study monitoring | Clinical interpretations | Reverse-scored items | Reliability analysis | Tidyverse integration

Simulated longitudinal dataset | scores_wide() vs scores_long() | Monitoring completion across time points | Preparing a panel data frame | Plotting change over time | Adding clinical interpretations

Why internal consistency matters | Cronbach's alpha | Interpreting alpha | McDonald's omega | When to use which | Comparing alpha and omega side by side | Using an items_long() data frame directly | Handling non-numeric items | Failure modes | References

Overview | Loading example data | Diary data: long format | Diary data: wide format | Participant-level summary | Clinical flags | Questionnaire scores | Accessing a single participant | Re-scoring all questionnaires | Next steps