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

Overview | 1. Simulate a twin cohort | 2. Build the GRM with MZ relatedness | 3. A vs C identifiability: herit_ace() | Covariates and automatic screening | 4. Bivariate genetic/environmental correlation: herit_bivar() | 5. Many trait pairs at once: herit_bivar_batch() | 6. A note on validation | 7. Citation

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

The pipeline in one call: compute_lids() | Under the hood: a clean synthetic example | From activity to LIDS: lids_transform() | Where bouts come from | Quality filtering | Study-level summaries | Next steps

Overview | P-value style | Decimal places | Significance stars | FDR display | Domain labels for unassigned variables | Nested domains (sub-sections within a domain) | Custom footnotes | 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

The processing chain | A synthetic recording | GT3X+-style filtering instead of ActTrust-style | From counts to PA intensity | Tuning zcm_threshold / tat_threshold | Next steps

The equation | Estimating METs from counts | Classifying PA intensity | Applying it to a real recording | Choosing an equation set | Next steps

What axR does | Discovering devices | Checking device status | Settings | Erasing a device | Downloading recorded data | If discovery isn't finding your device | Reading .cwa files | Handing off to zeitR | A note on scope | Known limitations

Three entropy measures, three different questions | Sample entropy: template matching | Why one number isn't enough: the multiscale idea | Choosing m and r | References

Overview | Installation | Example data | Fractal & multifractal analysis | Detrended Fluctuation Analysis | Higuchi Fractal Dimension | Multifractal Detrending Moving Average (MFDMA) | Chhabra-Jensen multifractal spectrum | Nonlinear time-domain features | Entropy | Recurrence quantification analysis (RQA) | Simulating test signals | Colour palette & theme | What isn't here yet

Two estimators, one underlying idea | Ground truth, not just plausible-looking data | MFDMA on the ground-truth series | Chhabra-Jensen on the same series | Which one should I use? | References

Overview | Installation | From mrpheus to hypnoR | Preprocessing | Smoothing | Windowing | Sleep architecture | Stage transitions | NREM/REM cycles | Plotting | Coarse hypnograms from zeitR | What isn't here yet

Setup | Single-column actogram | Double-plotted actogram | Activity double-plotted actogram | Colour customisation | Inspecting the default palette | Overriding individual colours | Suppressing naps | Date labels and font size | Adding ggplot2 layers | Working with a bare tibble | Choosing between the three variants | Next steps

Holiday formats | The no-holidays warning | Passing holidays to the pipeline | Auto-forwarding with S3 dispatch | Effect on metrics | Batch studies spanning multiple years | Non-standard schedules with free_days | Summary | See also

Background | The rule set | Basic usage | Comparing the two pipelines | Sleep onset and offset statistics | Advanced: running each step directly | Batch processing | Next steps

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

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

Data | Standard pipeline | Customising the pipeline | Using the signal-level functions independently | DC removal | Powerline detection | Notch filtering | Bandpass filtering | A note on filter design | EOG artefact correction | Regression-based correction | ICA-based correction | Which method to choose? | Full pipeline | References

Data | Setup | Band power per epoch | Relative power | Band power by sleep stage | Custom bands | Welch parameters | Temporal band power | Customising the window | Visualising temporal dynamics | Combining band power and staging | Parity with YASA | References

Data | Option A — via MNE Python (recommended) | Option B — direct download | Pipeline | 1. Load and inspect the recording | 2. Segment into 30-second epochs | 3. Stage epochs | 4. With artefact exclusion | Working with results | Stage distribution | Posterior probabilities | Visualising the night | Passing to hypnor | Model details | References

What is mrpheus? | Installation | PSG pipeline | Reading a recording | Preparing a recording | Artefact detection | Spectral analysis | Sleep event detection | Automatic sleep staging | Exporting to hypnor | Full PSG pipeline | MRI physiology pipeline | References

Reading a Philips PMU log | Signal channels | Event markers | QRS detection | PPU as an alternative | Heart rate signal | Epoch extraction | RR intervals per TR | Full pipeline | References

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

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()

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

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

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

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