NEWS

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mrpheus 0.1.4

Rcpp hot-path optimisation (staging pipeline + event detection)

Complete C++ hot-path port across the staging and event detection pipelines, replacing the remaining R-level bottlenecks. zoo removed from Imports.

Staging pipeline — per-epoch functions (src/staging_features.cpp):

• nzc_cpp: zero-crossing count — single O(N) pass replacing sum(diff(sign(x)) != 0)
• petrosian_fd_cpp: local extrema count + log formula — replaces diff + logical sum
• hjorth_cpp: mobility and complexity — single O(N) pass accumulating all three variances (x, diff(x), diff(diff(x))) without materialising intermediate vectors
• stat_features_cpp: std, IQR (type-7 quantile), skewness, excess kurtosis — replaces sd(), IQR() (which sorts), and two mean() calls per epoch
• rowmedian_cpp: row-wise median of the Welch periodogram matrix — eliminates ~750 000 R median() dispatch calls per recording (251 freq bins x ~3 000 epochs)
• roll_right_mean_cpp: right-aligned partial rolling mean (k=4), replacing zoo::rollapply for the _p2min_norm normalisation step (49 calls/recording)
• robust_scale_cpp: (x - median) / (q95 - q5) with type-7 quantile and NA-awareness, replacing median() + quantile() for 49 normalisation calls

Event detection (src/event_detection.cpp, new file):

• roll_rms_cpp: centered rolling RMS replacing zoo::rollapply in compute_spindles() — processes the full concatenated epoch signal per channel
• detect_so_candidates_cpp: zero-crossing scan + duration/amplitude filtering for slow oscillation detection, replacing the R which() + lapply loop in compute_slow_oscillations()

Other changes:

• Fixed a latent bug where the pure-R vapply definition of .roll_triang_mean in the normalisation section was shadowing roll_triang_mean_cpp on every load_all(). Now a single Rcpp-backed definition exists.
• Removed zoo from Imports — no remaining zoo:: calls anywhere in the package.

All 371 tests pass. 0 errors | 0 warnings | 0 notes.

mrpheus 0.1.3

PSG preprocessing pipeline

Full preprocessing pipeline from EDF to analysis-ready epochs, matching the companion Python/MNE notebook workflow.

• preprocess_psg() — continuous-signal preprocessing pipeline: channel renaming, DC removal, powerline notch filter (auto-detected 50/60 Hz + harmonics), and channel-type-specific bandpass filtering (EEG/EOG/EMG/ECG). Operates on the full continuous signal before re-epoching to avoid filter discontinuities at epoch boundaries.
• remove_dc(), detect_powerline(), notch_filter(), bandpass_filter() — exported signal-level functions; each operates on a plain numeric vector independently of the PSG pipeline.