zeitR: Actigraphy Data Parsing and Analysis for R

🚀 CI

• R-CMD-check.yaml's matrix expanded to mirror axR's coverage: devel (ubuntu, windows), release (ubuntu, windows, macOS), and oldrel-1 (windows, macOS) -- up from a release-only, three-platform matrix. New wasm-build job (r-wasm/actions/build-rwasm@v3) catches WebAssembly/webR build failures directly in this repo's own CI, rather than only discovering them after r-universe attempts its own wasm build. Unlike axR, no chmod +x configure cleanup step or additional libudev-dev install was needed for the new jobs themselves -- zeitR has no vendored C library with a configure script; the existing libudev-dev step stays as-is, there only for axR as a Suggests dependency.

✨ New features

• New LIDS (Locomotor Inactivity During Sleep) module -- lids_transform(), fit_lids(), detect_lids_bouts(), compute_lids(), and study_lids_metrics() -- porting the ultradian-rhythm methodology of Winnebeck et al. (2018, Current Biology) and its infant extension in Hammad et al. (2026, SLEEP, https://zenodo.org/records/18199381). lids_transform() applies the 100/(1+x) non-linear transform plus Gaussian (Hammad 2026, default) or moving-average (Winnebeck 2018 / pyActigraphy) smoothing; fit_lids() scans candidate periods (30-180 min by default) with an OLS sloped-cosine fit, selecting the period with the highest Munich Rhythmicity Index. compute_lids() is the main entry point: it extracts sleep bouts either from an existing zeitR pipeline's state column (bout_source = "state") or via the new standalone detect_lids_bouts() Roenneberg relative-immobility detector (bout_source = "roenneberg", for raw activity that hasn't been run through run_pipeline()/run_pipeline_native()), fits each bout, and applies the Winnebeck/Hammad quality filter (pearson_r, p_value, offset bounds). study_lids_metrics() is the batch/syncR::sync()-ready counterpart, summarising each participant's quality-filtered bouts (median +/- IQR period, amplitude, offset, slope) into one row, alongside study_sleep_metrics() and study_summary(). Ported from a prototype R notebook draft by Mario Leocadio-Miguel (itself adapted from an older MATLAB script); one bug in that draft is fixed here -- its bout-fusing step was truncated mid-statement (fused.a...) and would not have run as written; see ?detect_lids_bouts for the full reimplementation (.fuse_bouts()). Not yet validated against pyActigraphy's LIDS class or an external reference dataset -- treat results accordingly until a parity check is run.